Compress verbose logs, traces, and repeated output into a smaller context block before the agent retries a task.
Endpoint /api/v1/agent-tools/context-compress/
Input text, maxLength
Why agents buy it Use when a coding agent is about to resend a large terminal log, CI trace, or repo summary to an LLM.
Economic value Cuts repeated lines and clamps context size before another expensive model call.
Request and response sample
Request {
"text": "Downloading package...\nDownloading package...\nDownloading package...\nDone.",
"maxLength": 1200
}
Response {
"compressedText": "Downloading package...\nDownloading package...\nDownloading package...\nDone.",
"originalLength": 74,
"compressedLength": 74,
"compressionRatio": 1
}
Check whether a proposed replacement has exactly one matching target block before an agent edits a file.
Endpoint /api/v1/agent-tools/patch-verify/
Input originalContent, targetContent, replacementContent, allowMultiple
Why agents buy it Use before applying search-and-replace patches in an unknown workspace.
Economic value Prevents ambiguous edits and reduces failed repair loops after a bad patch.
Request and response sample
Request {
"originalContent": "const x = 5;\nconsole.log(x);",
"targetContent": "console.log(x);",
"replacementContent": "console.log('value', x);"
}
Response {
"success": true,
"updatedContent": "const x = 5;\nconsole.log('value', x);",
"occurrences": 1
}
Repair common LLM JSON failures: markdown fences, trailing commas, and unclosed arrays or objects.
Endpoint /api/v1/agent-tools/json-repair/
Input rawText or string
Why agents buy it Use when an agent received malformed JSON and wants a deterministic repair before asking the model again.
Economic value Avoids a full LLM retry for dirty but recoverable structured output.
Request and response sample
Request {
"rawText": "```json\n{\"status\":\"ok\",\"items\":[1,2,\n```"
}
Response {
"parsed": {
"status": "ok",
"items": [
1,
2
]
},
"repaired": true
}
Classify risky shell commands and clamp execution options before an agent launches a local process.
Endpoint /api/v1/agent-tools/shell-guard/
Input command, timeoutMs, maxBufferMb
Why agents buy it Use when an agent generated a terminal command and needs a cheap safety check before execution.
Economic value Blocks obvious destructive patterns and saves recovery time after unsafe commands.
Request and response sample
Request {
"command": "pnpm exec astro check",
"timeoutMs": 60000,
"maxBufferMb": 80
}
Response {
"safe": true,
"nodeOptions": {
"timeout": 60000,
"maxBuffer": 83886080,
"killSignal": "SIGTERM",
"windowsHide": true
},
"wrappedCommand": "pnpm exec astro check",
"warnings": []
}
Parse TypeScript, Astro, and linter output into structured diagnostics that another agent can patch.
Endpoint /api/v1/agent-tools/lint-error-heal/
Input errorLog
Why agents buy it Use after a failed check/build command to turn noisy output into machine-actionable JSON.
Economic value Reduces the amount of raw terminal output sent into the next model turn.
Request and response sample
Request {
"errorLog": "src/pages/foo.astro:12:8 - error TS6133: 'unused' is declared but never used."
}
Response {
"diagnostics": [
{
"file": "src/pages/foo.astro",
"line": 12,
"column": 8,
"code": "TS6133",
"message": "'unused' is declared but never used."
}
]
}
Extract missing npm dependencies from source code and return a cautious install command with typosquat warnings.
Endpoint /api/v1/agent-tools/dependency-plan/
Input code, existingDependencies
Why agents buy it Use when an agent generated code with imports and wants the smallest safe install step.
Economic value Avoids package guessing and reduces install-command retries.
Request and response sample
Request {
"code": "import sharp from 'sharp';\nimport fs from 'node:fs';",
"existingDependencies": [
"astro"
]
}
Response {
"missingDependencies": [
"sharp"
],
"installCommands": [
"pnpm add sharp"
],
"safeToInstall": true,
"warnings": []
}
Compile-check JavaScript or TypeScript-like code without executing it before an agent commits a patch.
Endpoint /api/v1/agent-tools/syntax-validate/
Input code, filename
Why agents buy it Use after generating a code block and before applying it to a repository.
Economic value Catches syntax failures locally before another repair conversation.
Request and response sample
Request {
"code": "const add = (a, b) => a + b;",
"filename": "patch.js"
}
Response {
"isValid": true,
"reason": "Syntax check passed"
}
Estimate branch complexity from a source block and return a simple grade for agent triage.
Endpoint /api/v1/agent-tools/complexity-score/
Input code
Why agents buy it Use when an agent needs to decide whether to patch a function directly or split it first.
Economic value Gives a cheap complexity signal without asking a model to reread the whole file.
Request and response sample
Request {
"code": "function ok(a){ if (a) return 1; return 0; }"
}
Response {
"complexity": 2,
"grade": "A"
}
Strip comments, collapse whitespace, and enforce a hard character budget with middle truncation.
Endpoint /api/v1/agent-tools/token-budget/
Input content, maxCharacters, stripComments
Why agents buy it Use before packing code, logs, or docs into a constrained agent context window.
Economic value Reduces prompt size while preserving the beginning and ending of long payloads.
Request and response sample
Request {
"content": "// note\nconst a = 1; \n\nconst b = 2;",
"maxCharacters": 100
}
Response {
"optimized": "const a = 1; \nconst b = 2;",
"originalSize": 34,
"optimizedSize": 26,
"savingsPercent": 24
}
Compare .env content against an example file and flag missing keys, empty keys, and public secret leaks.
Endpoint /api/v1/agent-tools/env-validate/
Input envContent, envExampleContent
Why agents buy it Use before deploying or sharing an environment summary with another agent.
Economic value Prevents leaking secrets into public client variables and avoids deployment retries caused by missing config.
Request and response sample
Request {
"envContent": "DATABASE_URL=\nPUBLIC_API_KEY=abc",
"envExampleContent": "DATABASE_URL=\nSECRET_TOKEN="
}
Response {
"isValid": false,
"missingKeys": [
"SECRET_TOKEN"
],
"emptyKeys": [
"DATABASE_URL"
],
"leaks": [],
"warnings": [
"Empty key: DATABASE_URL",
"Missing key: SECRET_TOKEN"
]
}
Calculate exponential retry delays with optional jitter for 429 and transient API failures.
Endpoint /api/v1/agent-tools/retry-backoff/
Input attempt, baseDelayMs, maxDelayMs, enableJitter, mockRandom
Why agents buy it Use when an agent is orchestrating repeated API calls and needs a deterministic delay plan.
Economic value Avoids asking a model to invent retry timing and reduces accidental request bursts.
Request and response sample
Request {
"attempt": 3,
"baseDelayMs": 1000,
"maxDelayMs": 16000,
"mockRandom": 0.5
}
Response {
"attempt": 3,
"retryDelayMs": 4000,
"maxRangeMs": 8000
}
Generate cautious backup and rollback command plans for dirty git workspaces.
Endpoint /api/v1/agent-tools/git-transaction/
Input action, statusOutput, commitMessage, backupBranch, timestamp
Why agents buy it Use before an autonomous agent starts a risky multi-file refactor.
Economic value Turns git safety into a reusable plan instead of spending context on manual recovery reasoning.
Request and response sample
Request {
"action": "backup",
"statusOutput": "modified: src/pages/index.astro",
"commitMessage": "Pre-refactor",
"timestamp": 1718000000000
}
Response {
"success": true,
"requiresBackup": true,
"branchName": "agent-backup-1718000000000",
"commands": [
"git checkout -b agent-backup-1718000000000",
"git add .",
"git commit -m \"Pre-refactor (1718000000000)\"",
"git checkout -"
]
}
Resolve simple git conflict-marker blocks using ours, theirs, or concat strategy.
Endpoint /api/v1/agent-tools/conflict-resolve/
Input content, strategy
Why agents buy it Use when an agent needs a mechanical conflict resolution before deeper semantic review.
Economic value Removes marker noise cheaply and lets the next model turn focus on semantics.
Request and response sample
Request {
"content": "line 1\n<<<<<<< HEAD\nours\n=======\ntheirs\n>>>>>>> branch\nline 3",
"strategy": "ours"
}
Response {
"success": true,
"resolvedContent": "line 1\nours\nline 3",
"hadConflicts": true
}
Scan model-generated code or shell scripts for destructive commands, credential access, and unsafe runtimes.
Endpoint /api/v1/agent-tools/action-guard/
Input code, language
Why agents buy it Use before allowing an agent-generated action to touch a filesystem, shell, or deployment target.
Economic value Blocks risky actions before they create expensive recovery work.
Request and response sample
Request {
"code": "rm -rf /var/log/nginx && systemctl restart nginx",
"language": "bash"
}
Response {
"isSafe": false,
"blockedPatterns": [
"destructive-command"
],
"securityLevel": "blocked"
}
Classify simple English and Russian toxicity/profanity patterns in text before an agent posts or stores output.
Endpoint /api/v1/agent-tools/toxicity-check/
Input text
Why agents buy it Use as a cheap pre-filter before publishing generated comments, support replies, or extracted user content.
Economic value Avoids routing obvious toxic text into a larger moderation model.
Request and response sample
Request {
"text": "Hello, thank you for the documentation."
}
Response {
"isToxic": false,
"score": 0,
"flaggedCategories": []
}
Extract and normalize phone numbers from CSV text into E.164-like strings with validation flags.
Endpoint /api/v1/agent-tools/csv-phone-clean/
Input rawText or string
Why agents buy it Use when an agent has scraped or imported contact CSV rows and needs a structured cleanup pass.
Economic value Avoids spending model tokens on repetitive phone-number normalization.
Request and response sample
Request {
"csvText": "name,phone\nAlice,+1 (555) 019-2834\nBob,123-456"
}
Response {
"results": [
{
"raw": "+1 (555) 019-2834",
"clean": "+15550192834",
"isValid": true
},
{
"raw": "123-456",
"clean": "+123456",
"isValid": false
}
]
}
Estimate model call costs from input and output token counts using a small configurable rate table.
Endpoint /api/v1/agent-tools/cost-estimate/
Input model, inputTokens, outputTokens, inputPricePerMillion, outputPricePerMillion, rates
Why agents buy it Use when an agent needs to choose between a cheap deterministic tool call and another model retry.
Economic value Makes the cost tradeoff explicit before the agent spends tokens.
Request and response sample
Request {
"model": "custom-large-model",
"inputTokens": 100000,
"outputTokens": 50000,
"inputPricePerMillion": 2.5,
"outputPricePerMillion": 10
}
Response {
"model": "custom-large-model",
"inputCost": 0.25,
"outputCost": 0.5,
"totalCost": 0.75
}
Score a page or service for AI Search, browser-agent usability, x402 payment discovery, MCP, A2A, and UCP readiness.
Endpoint /api/v1/agent-tools/agent-web-readiness-audit/
Input url, html, robotsTxt, headers, discoveredUrls
Why agents buy it Use before submitting a paid API, storefront, or content hub to agentic discovery channels.
Economic value Turns a large manual GEO and agent-commerce checklist into a deterministic JSON report.
Request and response sample
Request {
"url": "https://example.com/agent-tools/",
"html": "<html><head><title>Agent Tools</title><meta name=\"description\" content=\"Paid tools\"><link rel=\"alternate\" type=\"application/json\" href=\"/agent-tools.json\"><script type=\"application/ld+json\">{\"@type\":\"OfferCatalog\"}</script></head><body><main><h1>Agent Tools</h1><a href=\"/.well-known/agent-card.json\">Agent Card</a><a href=\"/.well-known/mcp/server-card.json\">MCP</a><a href=\"/.well-known/ucp\">UCP</a><button>Buy</button></main></body></html>",
"robotsTxt": "User-agent: *\nAllow: /\nSitemap: https://example.com/sitemap.xml",
"discoveredUrls": [
"https://example.com/.well-known/agent-card.json",
"https://example.com/.well-known/mcp/server-card.json",
"https://example.com/.well-known/ucp"
]
}
Response {
"score": 92,
"grade": "A",
"channelReadiness": {
"aiSearch": "ready",
"browserAgents": "ready",
"x402Bazaar": "partial",
"mcp": "ready",
"a2a": "ready",
"ucp": "ready"
},
"findings": [
{
"id": "title-description",
"status": "pass",
"severity": "info",
"message": "Title and meta description are present."
}
],
"recommendedEndpoints": [
"https://example.com/.well-known/agent-card.json",
"https://example.com/.well-known/mcp/server-card.json",
"https://example.com/.well-known/ucp"
]
}
Validate an x402 v2 payment requirement and Bazaar-facing resource metadata before agents discover or buy it.
Endpoint /api/v1/agent-tools/x402-bazaar-metadata-lint/
Input paymentRequired, resourceUrl, inputSchema, outputSchema
Why agents buy it Use before publishing a paid endpoint to Bazaar-style x402 discovery so the offer has a clean resource, scheme, network, amount, schemas, and buyer hints.
Economic value Avoids a long agent retry loop by turning malformed x402 metadata into exact missing fields and normalized hints.
Request and response sample
Request {
"paymentRequired": {
"x402Version": 2,
"resource": {
"url": "https://example.com/api/v1/agent-tools/audit/"
},
"accepts": [
{
"scheme": "exact",
"network": "eip155:8453",
"amount": "5000",
"asset": "0x833589fcd6edb6e08f4c7c32d4f71b54bda02913",
"payTo": "0xd9d6ae1d5a2128fea511460fac8e4dea58baf153"
}
],
"extensions": {
"bazaar": {
"description": "Agent web readiness audit",
"inputSchema": {
"type": "object"
},
"outputSchema": {
"type": "object"
}
}
}
}
}
Response {
"score": 92,
"status": "ready",
"findings": [
{
"id": "x402-version",
"status": "pass",
"message": "x402 v2 signal is present."
}
],
"missingFields": [],
"normalizedBazaarHints": {
"resource": "https://example.com/api/v1/agent-tools/audit/",
"network": "eip155:8453",
"amountAtomic": "5000",
"priceUSDC": 0.005,
"requiredHeaders": [
"PAYMENT-SIGNATURE",
"X-PAYMENT"
]
}
}
Score an MCP server card for discovery completeness, callable tools, schema clarity, transport metadata, and paid-tool readiness.
Endpoint /api/v1/agent-tools/mcp-server-card-audit/
Input serverCard, endpointUrl, expectedTools
Why agents buy it Use before exposing a Model Context Protocol server to agents that need to discover, price, and call tools without human glue code.
Economic value Compresses MCP discovery review into a machine-readable checklist with exact card fields to add.
Request and response sample
Request {
"endpointUrl": "https://example.com/api/mcp",
"expectedTools": [
"agent-web-readiness-audit"
],
"serverCard": {
"protocolVersion": "2025-06-18",
"serverInfo": {
"name": "Example MCP"
},
"transport": {
"type": "streamable-http",
"endpoint": "https://example.com/api/mcp"
},
"tools": [
{
"name": "agent-web-readiness-audit",
"description": "Audit agent web readiness.",
"inputSchema": {
"type": "object"
}
}
]
}
}
Response {
"score": 88,
"grade": "B",
"findings": [
{
"id": "tools",
"status": "pass",
"message": "Tool list is discoverable."
}
],
"recommendedFields": [
"pricing",
"authentication",
"x402Payment"
],
"toolCoverage": {
"expected": 1,
"found": 1,
"described": 1,
"schemaReady": 1
}
}
Compare a site against agent-readable discovery surfaces: Agent Card, MCP, UCP, OfferCatalog, robots, llms.txt, raw context, and paid endpoints.
Endpoint /api/v1/agent-tools/agent-discovery-gap-report/
Input url, agentCard, mcpServerCard, ucpProfile, offerCatalog, robotsTxt, llmsTxt
Why agents buy it Use when an agent needs to decide which discovery layer to fix first for AI Search, Bazaar-style marketplaces, and browser agents.
Economic value Converts scattered discovery signals into a ranked crawl plan and missing-surface list.
Request and response sample
Request {
"url": "https://example.com",
"robotsTxt": "User-agent: *\nAllow: /\nSitemap: https://example.com/sitemap.xml",
"llmsTxt": "# Example\n- /agent-tools.json"
}
Response {
"score": 64,
"missingSurfaces": [
"agentCard",
"mcpServerCard",
"ucpProfile"
],
"priorityFixes": [
"Publish /.well-known/agent-card.json.",
"Expose /.well-known/mcp/server-card.json."
],
"crawlPlan": [
{
"path": "/agent-tools.json",
"purpose": "Paid offer catalog",
"priority": "high"
}
]
}
Audit llms.txt and raw text endpoints for token shape, canonical source hints, section quality, and context-buying usefulness.
Endpoint /api/v1/agent-tools/llms-raw-context-audit/
Input url, llmsTxt, rawText, maxRecommendedBytes
Why agents buy it Use before submitting raw content to AI retrieval systems or selling compact context blocks to agents.
Economic value Finds oversized, vague, or badly linked context before another agent wastes tokens reading it.
Request and response sample
Request {
"url": "https://example.com/llms.txt",
"llmsTxt": "# Example\n- [Agent tools](https://example.com/agent-tools.json): Paid tools for agents.",
"rawText": "Canonical URL: https://example.com/articles/example\nSummary: ...",
"maxRecommendedBytes": 120000
}
Response {
"score": 86,
"tokenShape": {
"characters": 126,
"estimatedTokens": 32,
"rawBytes": 65,
"withinLimit": true
},
"findings": [
{
"id": "links",
"status": "pass",
"message": "Machine-readable links are present."
}
],
"suggestedSections": [
"Canonical URL",
"Summary",
"Tools",
"Pricing",
"Evidence"
],
"compressionHints": [
"Keep repeated boilerplate out of raw context."
]
}
Score crawler policies for AI search, user-triggered fetchers, training bots, and paid-agent allowlists.
Endpoint /api/v1/agent-tools/ai-crawler-policy-audit/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when a site wants to welcome useful agents while separating indexing, training, and paid access.
Economic value Turns robots and AI policy text into exact allow/block/publish actions.
Request and response sample
Request {
"subject": "AI crawler policy for a paid tool catalog",
"context": "robots.txt allows public pages, disallows private API paths, and mentions ChatGPT-User plus OAI-SearchBot.",
"goal": "Separate discovery from paid execution."
}
Response {
"tool": "ai-crawler-policy-audit",
"subject": "AI crawler policy for a paid tool catalog",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "AI Crawler Policy Audit has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Check Schema.org OfferCatalog, SoftwareApplication, Product, Dataset, and Action metadata for machine buyers.
Endpoint /api/v1/agent-tools/schema-offer-catalog-audit/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before agents or AI search systems need to understand what is sold, how much it costs, and how to call it.
Economic value Saves token-heavy manual inspection of JSON-LD and catalog consistency.
Request and response sample
Request {
"subject": "JSON-LD offer catalog for paid agent tools",
"context": "Catalog has itemListElement entries, prices in USDC, endpoint URLs, request schemas, and response schemas.",
"goal": "Improve agent marketplace indexing."
}
Response {
"tool": "schema-offer-catalog-audit",
"subject": "JSON-LD offer catalog for paid agent tools",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Schema Offer Catalog Audit has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Suggest x402 micropayment tiers from latency, token savings, buyer urgency, and result uniqueness.
Endpoint /api/v1/agent-tools/agent-paywall-pricing-advisor/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when choosing whether a callable endpoint should cost 0.003, 0.02, 0.10, or more USDC.
Economic value Avoids a pricing guess by mapping buyer utility to low-friction machine-payment tiers.
Request and response sample
Request {
"subject": "Pricing for an agent-web readiness audit",
"context": "The tool saves one to three model calls and returns a deterministic checklist in under 400ms.",
"goal": "Set a low entry price with a path to higher-value batch reports."
}
Response {
"tool": "agent-paywall-pricing-advisor",
"subject": "Pricing for an agent-web readiness audit",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Agent Paywall Pricing Advisor has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Lint MCP tool names, descriptions, input schemas, output contracts, and error semantics for reliable agent use.
Endpoint /api/v1/agent-tools/mcp-tool-schema-lint/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before exposing tools to MCP clients that decide based on descriptions and JSON schemas.
Economic value Prevents agent miscalls caused by vague names, weak schemas, and missing error contracts.
Request and response sample
Request {
"subject": "MCP tool schema for a paid audit endpoint",
"context": "Tool has name, description, inputSchema, price metadata, and examples.",
"goal": "Make the MCP listing self-explanatory to buyer agents."
}
Response {
"tool": "mcp-tool-schema-lint",
"subject": "MCP tool schema for a paid audit endpoint",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "MCP Tool Schema Lint has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Score a dataset description for freshness, coverage, columns, provenance, missingness, and buyer-ready documentation.
Endpoint /api/v1/agent-tools/dataset-quality-score/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before an agent buys, joins, or recommends a dataset.
Economic value Summarizes data readiness without loading the whole dataset into a model context.
Request and response sample
Request {
"subject": "Crypto exchange fee dataset",
"context": "CSV has exchange, pair, makerFee, takerFee, updatedAt, and sourceUrl columns.",
"goal": "Decide whether the dataset is safe to enrich a market report."
}
Response {
"tool": "dataset-quality-score",
"subject": "Crypto exchange fee dataset",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Dataset Quality Score has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Compare expected and observed CSV columns, types, row counts, null rates, and breaking changes.
Endpoint /api/v1/agent-tools/csv-schema-drift-check/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when an agent receives a fresh CSV and needs to know if downstream automations will break.
Economic value Replaces a full model pass over rows with a concise drift report.
Request and response sample
Request {
"subject": "Daily product feed schema",
"context": "Expected sku,name,price,currency; observed sku,title,price_usd,currency,stock.",
"goal": "Detect breaking changes before import."
}
Response {
"tool": "csv-schema-drift-check",
"subject": "Daily product feed schema",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "CSV Schema Drift Check has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Review API response examples for stable fields, typed errors, pagination, rate-limit hints, and agent-friendly contracts.
Endpoint /api/v1/agent-tools/api-response-contract-audit/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before publishing an endpoint that agents will call repeatedly.
Economic value Prevents wasted retries caused by ambiguous response shapes.
Request and response sample
Request {
"subject": "Paid pricing intelligence API response",
"context": "Response includes data, error, requestId, nextPageToken, and rateLimit.",
"goal": "Make the API contract resilient for autonomous callers."
}
Response {
"tool": "api-response-contract-audit",
"subject": "Paid pricing intelligence API response",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "API Response Contract Audit has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Identify likely personal data in a sample and recommend deterministic redaction rules before sharing context with agents.
Endpoint /api/v1/agent-tools/pii-redaction-plan/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before feeding support logs, CRM exports, or user content into another model or paid tool.
Economic value Reduces privacy review into actionable regex and field-level redaction hints.
Request and response sample
Request {
"subject": "Support ticket export",
"context": "Tickets contain emails, phone numbers, order IDs, IPs, and free-text complaint fields.",
"goal": "Prepare a safer context block for an agent workflow."
}
Response {
"tool": "pii-redaction-plan",
"subject": "Support ticket export",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "PII Redaction Plan has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Summarize revenue, gross margin, payment fees, compute cost, refund risk, and contribution margin from compact inputs.
Endpoint /api/v1/agent-tools/unit-economics-snapshot/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when an agent needs to decide if a paid API, microservice, or product can profit at the current price.
Economic value Turns scattered business inputs into a quick machine-readable economics verdict.
Request and response sample
Request {
"subject": "x402 paid audit endpoint",
"context": "Price 0.005 USDC, median compute cost 0.0003, facilitator and infra overhead low, refund rate unknown.",
"goal": "Check whether entry pricing can still scale profitably."
}
Response {
"tool": "unit-economics-snapshot",
"subject": "x402 paid audit endpoint",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Unit Economics Snapshot has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Check whether stated acquisition cost, conversion, retention, gross margin, and payback assumptions are plausible.
Endpoint /api/v1/agent-tools/ltv-cac-sanity-check/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before a growth agent recommends spending budget on traffic, ads, affiliates, or agent marketplaces.
Economic value Replaces a long spreadsheet-style reasoning turn with a structured assumptions check.
Request and response sample
Request {
"subject": "Agent-tool marketplace launch",
"context": "CAC expected near zero from crawler discovery, repeat usage uncertain, gross margin above 80%.",
"goal": "Prioritize acquisition channels."
}
Response {
"tool": "ltv-cac-sanity-check",
"subject": "Agent-tool marketplace launch",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "LTV CAC Sanity Check has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Review pricing copy, plan structure, proof, friction, and buyer intent match for human and agent visitors.
Endpoint /api/v1/agent-tools/pricing-page-conversion-audit/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before sending traffic to a pricing page or machine-readable offer page.
Economic value Produces a concise conversion checklist from page text and plan metadata.
Request and response sample
Request {
"subject": "ELPA agent tools pricing block",
"context": "Page lists 64 paid endpoints, USDC prices, schemas, latency, and x402 headers.",
"goal": "Increase calls from buyer agents and technical users."
}
Response {
"tool": "pricing-page-conversion-audit",
"subject": "ELPA agent tools pricing block",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Pricing Page Conversion Audit has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Analyze a small basket of SKUs for gross margin, bundle candidates, price anomalies, and low-margin risk.
Endpoint /api/v1/agent-tools/market-basket-margin-scan/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when an ecommerce agent needs a fast profitability check before making pricing or promo changes.
Economic value Avoids sending a full SKU table to an expensive model for first-pass triage.
Request and response sample
Request {
"subject": "Coffee accessories basket",
"context": "Items include retail price, landed cost, shipping estimate, conversion rate, and stock.",
"goal": "Find bundle and repricing opportunities."
}
Response {
"tool": "market-basket-margin-scan",
"subject": "Coffee accessories basket",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Market Basket Margin Scan has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Summarize currency exposure from revenue, costs, settlement currency, geography, and payout timing.
Endpoint /api/v1/agent-tools/fx-exposure-brief/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when an agent evaluates a cross-border business, vendor contract, or crypto/off-ramp flow.
Economic value Condenses financial risk into hedging questions and missing evidence.
Request and response sample
Request {
"subject": "USDC revenue with Thai operating costs",
"context": "Revenue settles in USDC, contractors are paid in THB, cloud costs are USD.",
"goal": "Identify currency mismatch and payout risk."
}
Response {
"tool": "fx-exposure-brief",
"subject": "USDC revenue with Thai operating costs",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "FX Exposure Brief has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Scan token supply, unlocks, incentives, concentration, utility, and narrative risk from a compact project brief.
Endpoint /api/v1/agent-tools/crypto-tokenomics-risk-scan/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before an agent drafts a crypto market note or ranks token opportunities.
Economic value Produces a structured risk map without a broad model research pass.
Request and response sample
Request {
"subject": "Agent-commerce utility token",
"context": "Supply capped, team unlock starts in six months, token used for marketplace fees.",
"goal": "Flag risks before writing an investment-style brief."
}
Response {
"tool": "crypto-tokenomics-risk-scan",
"subject": "Agent-commerce utility token",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Crypto Tokenomics Risk Scan has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Turn wallet activity notes into a cautious label brief with confidence, counterparties, and evidence gaps.
Endpoint /api/v1/agent-tools/onchain-wallet-label-brief/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when an agent has transaction summaries and needs a non-definitive wallet classification.
Economic value Avoids overclaiming from partial onchain evidence while preserving useful signals.
Request and response sample
Request {
"subject": "Base wallet activity summary",
"context": "Wallet receives USDC, calls paid APIs, has repeated small transfers, and no exchange deposit tag.",
"goal": "Classify likely role with confidence and caveats."
}
Response {
"tool": "onchain-wallet-label-brief",
"subject": "Base wallet activity summary",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Onchain Wallet Label Brief has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Score a project brief against grant or RFP requirements, eligibility, evidence, budget, and proposal gaps.
Endpoint /api/v1/agent-tools/grant-rfp-fit-score/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before an agent spends time drafting a grant, tender, or partnership response.
Economic value Prevents expensive proposal generation when eligibility or evidence is weak.
Request and response sample
Request {
"subject": "AI discovery tooling grant",
"context": "RFP asks for open standards, measurable adoption, security controls, and small-business eligibility.",
"goal": "Decide whether to draft."
}
Response {
"tool": "grant-rfp-fit-score",
"subject": "AI discovery tooling grant",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Grant RFP Fit Score has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Map competitors by buyer, promise, proof, pricing model, moat, and weak spots from compact descriptions.
Endpoint /api/v1/agent-tools/competitor-positioning-map/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when a strategy agent needs a first positioning grid before deeper research.
Economic value Compresses competitive notes into a ranked differentiation map.
Request and response sample
Request {
"subject": "x402 paid API marketplace tools",
"context": "Competitors include generic API marketplaces, crawler audits, and MCP server directories.",
"goal": "Find a sharper niche for ELPA."
}
Response {
"tool": "competitor-positioning-map",
"subject": "x402 paid API marketplace tools",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Competitor Positioning Map has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Score whether an API/tool offer is likely to sell in a machine-buyer marketplace based on urgency, repeatability, uniqueness, and proof.
Endpoint /api/v1/agent-tools/marketplace-offer-fit-score/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before listing a new endpoint in an x402, MCP, or API marketplace.
Economic value Rejects weak offers early and suggests stronger paid result shapes.
Request and response sample
Request {
"subject": "Paid raw context compressor",
"context": "The tool saves tokens, is deterministic, and can be used by any coding agent.",
"goal": "Estimate whether agents would buy it."
}
Response {
"tool": "marketplace-offer-fit-score",
"subject": "Paid raw context compressor",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Marketplace Offer Fit Score has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Rewrite page signals into answer-engine-friendly title, summary, facts, citations, and machine action hints.
Endpoint /api/v1/agent-tools/seo-geo-snippet-optimizer/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when an agent prepares a page for AI search and generative engine snippets.
Economic value Turns page text into structured snippet assets without a full content rewrite.
Request and response sample
Request {
"subject": "Agentic Web Stack page",
"context": "The page describes x402, MCP, UCP, OfferCatalog, and paid tools.",
"goal": "Improve answer-engine extraction."
}
Response {
"tool": "seo-geo-snippet-optimizer",
"subject": "Agentic Web Stack page",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "SEO GEO Snippet Optimizer has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Score product feeds for identifiers, titles, descriptions, prices, stock, images, schema, and marketplace readiness.
Endpoint /api/v1/agent-tools/product-feed-quality-audit/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when a commerce agent needs to decide if a feed can be indexed, matched, or repriced.
Economic value Finds feed issues before costly marketplace sync attempts.
Request and response sample
Request {
"subject": "Electronics product feed",
"context": "Feed includes GTIN, SKU, title, description, price, stock, image, and category.",
"goal": "Prepare for agent shopping surfaces."
}
Response {
"tool": "product-feed-quality-audit",
"subject": "Electronics product feed",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Product Feed Quality Audit has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Compare target query, buyer intent, hero copy, proof, CTA, and page structure for conversion alignment.
Endpoint /api/v1/agent-tools/landing-page-intent-match/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before routing paid or AI-search traffic to a page.
Economic value Detects mismatch without asking a large model to critique the full page.
Request and response sample
Request {
"subject": "Paid agent tools landing page",
"context": "Target query is x402 paid tools for AI agents; page lists endpoints and schemas.",
"goal": "Improve first-screen match."
}
Response {
"tool": "landing-page-intent-match",
"subject": "Paid agent tools landing page",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Landing Page Intent Match has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Compare a policy draft against required topics, operational controls, owner fields, and update cadence.
Endpoint /api/v1/agent-tools/policy-gap-scan/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when an agent drafts privacy, AI, security, or marketplace rules and needs a gap list.
Economic value Produces missing sections and evidence requests without legal overclaiming.
Request and response sample
Request {
"subject": "AI crawler and paid agent access policy",
"context": "Policy explains indexing, training, user-triggered fetches, paid APIs, and abuse handling.",
"goal": "Find missing governance sections."
}
Response {
"tool": "policy-gap-scan",
"subject": "AI crawler and paid agent access policy",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Policy Gap Scan has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Summarize vendor answers into risk flags, missing evidence, control owners, and follow-up questions.
Endpoint /api/v1/agent-tools/vendor-risk-questionnaire-summarize/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when procurement or security agents need triage before a human review.
Economic value Compresses long questionnaires into a concise risk JSON.
Request and response sample
Request {
"subject": "MCP hosting vendor questionnaire",
"context": "Vendor describes encryption, logging, incident response, data retention, and subprocessors.",
"goal": "Prepare follow-up questions."
}
Response {
"tool": "vendor-risk-questionnaire-summarize",
"subject": "MCP hosting vendor questionnaire",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Vendor Risk Questionnaire Summarize has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Map contract clauses into commercial, privacy, IP, liability, renewal, and termination risk buckets.
Endpoint /api/v1/agent-tools/contract-clause-risk-map/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when an agent needs a structured first pass before legal review.
Economic value Avoids full-contract reasoning when the task is triage and issue spotting.
Request and response sample
Request {
"subject": "API marketplace seller agreement",
"context": "Agreement covers fees, settlement, API uptime, refunds, liability, and content rights.",
"goal": "Identify clauses needing human review."
}
Response {
"tool": "contract-clause-risk-map",
"subject": "API marketplace seller agreement",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Contract Clause Risk Map has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Review cookie/banner copy for consent clarity, purpose categories, reject parity, and tracking disclosure gaps.
Endpoint /api/v1/agent-tools/gdpr-cookie-copy-audit/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before a web agent ships tracking or analytics changes in Europe-facing pages.
Economic value Turns banner text into practical missing-field checks.
Request and response sample
Request {
"subject": "Analytics consent banner",
"context": "Banner has accept all, manage choices, analytics cookies, and marketing cookies.",
"goal": "Check whether copy is likely too vague."
}
Response {
"tool": "gdpr-cookie-copy-audit",
"subject": "Analytics consent banner",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "GDPR Cookie Copy Audit has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Turn incident notes into timeline, impact, root-cause hypotheses, contributing factors, and corrective actions.
Endpoint /api/v1/agent-tools/incident-postmortem-draft/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when an operations agent needs a first postmortem structure after an outage or failed deployment.
Economic value Converts noisy incident notes into a compact postmortem without another broad LLM pass.
Request and response sample
Request {
"subject": "Failed x402 payment settlement rollout",
"context": "402 challenges worked, facilitator auth failed, clients saw invalid payment responses.",
"goal": "Create a no-blame corrective action list."
}
Response {
"tool": "incident-postmortem-draft",
"subject": "Failed x402 payment settlement rollout",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Incident Postmortem Draft has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Convert a role brief into structured competencies, interview signals, evaluation weights, and red flags.
Endpoint /api/v1/agent-tools/hiring-scorecard-builder/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before recruiting agents screen candidates or draft interview kits.
Economic value Creates a reusable hiring rubric from a compact role description.
Request and response sample
Request {
"subject": "Agentic commerce engineer",
"context": "Role needs TypeScript, x402, payments, MCP, Astro, and search/discovery instincts.",
"goal": "Build a consistent interview scorecard."
}
Response {
"tool": "hiring-scorecard-builder",
"subject": "Agentic commerce engineer",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Hiring Scorecard Builder has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Classify support tickets by urgency, revenue impact, abuse risk, missing data, and next action.
Endpoint /api/v1/agent-tools/support-ticket-priority-triage/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when a customer-support agent needs cheap deterministic triage before escalation.
Economic value Reduces ticket queues into priorities and data requests.
Request and response sample
Request {
"subject": "Payment failed for paid API call",
"context": "Customer says x402 payment was signed but endpoint returned 402 invalid_scheme.",
"goal": "Prioritize and route the ticket."
}
Response {
"tool": "support-ticket-priority-triage",
"subject": "Payment failed for paid API call",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Support Ticket Priority Triage has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Review account notes for usage decline, unresolved support, price friction, champion loss, and expansion blockers.
Endpoint /api/v1/agent-tools/churn-risk-signal-audit/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when a revenue agent needs a first churn-risk read from CRM or support notes.
Economic value Turns qualitative notes into structured retention actions.
Request and response sample
Request {
"subject": "API customer renewal risk",
"context": "Usage dropped 40%, payment errors unresolved, buyer asked about lower tiers.",
"goal": "Recommend save actions."
}
Response {
"tool": "churn-risk-signal-audit",
"subject": "API customer renewal risk",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Churn Risk Signal Audit has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Score property listing text for location clarity, amenities, photos, price context, compliance, and buyer-agent usefulness.
Endpoint /api/v1/agent-tools/real-estate-listing-quality-score/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before listing syndication or agent-assisted property search.
Economic value Finds missing fields without reading dozens of listings in a model turn.
Request and response sample
Request {
"subject": "Condo listing in Bangkok",
"context": "Listing includes district, BTS distance, bedrooms, floor, monthly fees, photos, and restrictions.",
"goal": "Improve agent search match."
}
Response {
"tool": "real-estate-listing-quality-score",
"subject": "Condo listing in Bangkok",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Real Estate Listing Quality Score has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Check itinerary notes for timing conflicts, transfer risk, visa/document gaps, weather sensitivity, and backup needs.
Endpoint /api/v1/agent-tools/travel-itinerary-risk-check/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when a travel agent builds or validates a trip plan.
Economic value Prevents itinerary mistakes before a more expensive planning pass.
Request and response sample
Request {
"subject": "Two-day Bangkok to Phuket itinerary",
"context": "Flight lands at 10:30, hotel check-in 15:00, ferry booking same afternoon, rainy season.",
"goal": "Identify timing and backup risks."
}
Response {
"tool": "travel-itinerary-risk-check",
"subject": "Two-day Bangkok to Phuket itinerary",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Travel Itinerary Risk Check has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Review health content for unsafe claims, missing disclaimers, emergency guidance gaps, and evidence language.
Endpoint /api/v1/agent-tools/healthcare-content-safety-audit/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before a content agent publishes health-adjacent text that needs extra caution.
Economic value Flags risky wording without attempting diagnosis or medical advice.
Request and response sample
Request {
"subject": "Article about sleep supplements",
"context": "Draft makes claims about insomnia, dosage, interactions, and long-term use.",
"goal": "Reduce unsafe or overconfident medical wording."
}
Response {
"tool": "healthcare-content-safety-audit",
"subject": "Article about sleep supplements",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Healthcare Content Safety Audit has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Score course outlines for learning objectives, prerequisites, sequence, assessments, practice loops, and accessibility.
Endpoint /api/v1/agent-tools/education-course-outline-audit/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before an education agent generates full lessons or sells a course plan.
Economic value Finds structural gaps before producing long learning content.
Request and response sample
Request {
"subject": "Intro to agentic web commerce course",
"context": "Modules cover x402, MCP, UCP, OfferCatalog, and crawler analytics.",
"goal": "Improve sequence and assessment design."
}
Response {
"tool": "education-course-outline-audit",
"subject": "Intro to agentic web commerce course",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Education Course Outline Audit has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Summarize flexible compute, tariff windows, battery/storage notes, and operational constraints into load-shift opportunities.
Endpoint /api/v1/agent-tools/energy-load-shift-opportunity/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when an infrastructure agent evaluates whether workloads can move to cheaper or cleaner power windows.
Economic value Turns energy/compute notes into a concise scheduling opportunity map.
Request and response sample
Request {
"subject": "Inference batch jobs",
"context": "Jobs can run overnight, latency is flexible, region has lower off-peak tariff.",
"goal": "Identify load-shifting wins."
}
Response {
"tool": "energy-load-shift-opportunity",
"subject": "Inference batch jobs",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Energy Load Shift Opportunity has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Review route notes for delay risk, handoff count, customs, cold-chain, fragile goods, and contingency needs.
Endpoint /api/v1/agent-tools/logistics-route-risk-brief/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before an operations agent picks a carrier or route.
Economic value Creates a route risk summary from compact shipment data.
Request and response sample
Request {
"subject": "Bangkok to Singapore electronics shipment",
"context": "Two carriers, one customs handoff, insured value high, delivery window tight.",
"goal": "Spot route and documentation risks."
}
Response {
"tool": "logistics-route-risk-brief",
"subject": "Bangkok to Singapore electronics shipment",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Logistics Route Risk Brief has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Scan a bill-of-materials summary for single-source parts, lead-time risk, cost concentration, and compliance gaps.
Endpoint /api/v1/agent-tools/manufacturing-bom-risk-scan/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when a sourcing agent needs first-pass supply-chain risk before procurement work.
Economic value Condenses BOM risk without loading a full spreadsheet into a model.
Request and response sample
Request {
"subject": "Edge device BOM",
"context": "MCU is single-sourced, enclosure has long tooling lead time, sensors have two alternates.",
"goal": "Find procurement risks."
}
Response {
"tool": "manufacturing-bom-risk-scan",
"subject": "Edge device BOM",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Manufacturing BOM Risk Scan has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Extract themes, bugs, feature requests, sentiment, and revenue-risk signals from compact review samples.
Endpoint /api/v1/agent-tools/app-store-review-miner/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when a product agent needs quick prioritization from user feedback.
Economic value Turns review text into ranked product actions without a broad analysis prompt.
Request and response sample
Request {
"subject": "Mobile wallet reviews",
"context": "Users mention failed payments, confusing backup phrase flow, slow support, and good exchange rates.",
"goal": "Rank product fixes."
}
Response {
"tool": "app-store-review-miner",
"subject": "Mobile wallet reviews",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "App Store Review Miner has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Map abstract or notes into claims, evidence type, limitations, replication needs, and citation targets.
Endpoint /api/v1/agent-tools/research-paper-claim-map/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when a research agent needs a compact paper triage before deeper reading.
Economic value Avoids pasting a whole paper into a model for first-pass structure.
Request and response sample
Request {
"subject": "Paper about agent search behavior",
"context": "Abstract claims agents prefer schema-rich pages and paid APIs with explicit contracts.",
"goal": "Extract claims and evidence gaps."
}
Response {
"tool": "research-paper-claim-map",
"subject": "Paper about agent search behavior",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Research Paper Claim Map has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Compare bids by price, delivery, risk, warranty, compliance, vendor history, and missing assumptions.
Endpoint /api/v1/agent-tools/procurement-bid-comparison/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use before a procurement agent ranks vendors or drafts negotiation points.
Economic value Compresses bid notes into a structured comparison grid.
Request and response sample
Request {
"subject": "Three cloud hosting bids",
"context": "Bids vary by monthly price, bandwidth, uptime SLA, support tier, and data-region promises.",
"goal": "Rank options and identify negotiation points."
}
Response {
"tool": "procurement-bid-comparison",
"subject": "Three cloud hosting bids",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Procurement Bid Comparison has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Score a local-service lead for intent, budget, urgency, geography, contact quality, and next response.
Endpoint /api/v1/agent-tools/local-services-lead-quality/
Input subject, context, url, data, signals, goal, constraints, timeframe, market, currency
Why agents buy it Use when a local business agent triages inbound leads before calling or quoting.
Economic value Turns messy form submissions into priority and follow-up actions.
Request and response sample
Request {
"subject": "Emergency plumbing inquiry",
"context": "Lead includes phone, district, leak severity, photos missing, and requested same-day service.",
"goal": "Prioritize and draft follow-up questions."
}
Response {
"tool": "local-services-lead-quality",
"subject": "Emergency plumbing inquiry",
"score": 78,
"grade": "B",
"confidence": "medium",
"findings": [
{
"id": "input-coverage",
"status": "warn",
"severity": "medium",
"message": "Local Services Lead Quality has enough context for first-pass triage, but live evidence would improve confidence."
}
],
"priorityActions": [
"Add source URLs or structured data for higher-confidence scoring.",
"Expose the result as JSON so downstream agents can act without parsing prose."
],
"missingInputs": [
"sourceUrls",
"freshMetrics"
],
"machineActions": [
{
"action": "collect_evidence",
"target": "sourceUrls",
"priority": "high"
}
],
"evidenceRequests": [
"Provide compact facts, source URLs, and any hard numeric metrics."
],
"monetizationAngle": "A small paid triage result can save one or more model calls when the buyer only needs a ranked decision."
}
Verify Stripe webhook signatures with HMAC SHA-256 and timestamp tolerance checks.
Endpoint /api/v1/agent-tools/stripe-signature/
Input rawBody, signatureHeader, webhookSecret, toleranceSeconds
Why agents buy it Use when an agent builds or tests webhook handlers and needs a cryptographic verification result.
Economic value Avoids model guessing around webhook security and replay windows.
Request and response sample
Request {
"rawBody": "{\"id\":\"evt_123\"}",
"signatureHeader": "t=1778000000,v1=wrongsignaturevaluehere",
"webhookSecret": "whsec_test",
"toleranceSeconds": 99999999999
}
Response {
"isValid": false,
"timestamp": 1778000000,
"reason": "Signature mismatch"
}
