Audit

The AgentReady Score for a tool, with four pillar sub-scores and the fixes.

audit rates how ready a tool is for AI agents. It returns the AgentReady Score, a single number from 0 to 100, plus a sub-score for each of the four pillars, the facts behind the score, the raw discovery signals, and a ranked list of fixes.

Use audit to check one tool you already have in mind. Use find_tools_for_task when you want ranked candidates for a job. For how the number is computed, see Methodology.

#The four pillars

Pillar	Score key	What it measures
DISCOVER	`discovery`	Can an agent find the tool and know what it does?
UNDERSTAND	`understanding`	Are the docs, schemas, and capabilities clear to an agent?
ADOPT	`adoption`	Can an agent sign up, get a key, and install without a human?
OPERATE	`operability`	Can an agent run it as a primitive: API, MCP, SDKs?

The headline agentready score is the average of the four pillar scores.

#Call it

curl "https://api.stackresolve.dev/v1/audit?domain=stripe.com" \
  -H "x-api-key: $STACKRESOLVE_API_KEY"

audit({ domain: "stripe.com" })

stackresolve audit stripe.com

#Parameters

domainstringrequired

The domain to score, for example stripe.com. A bare slug also works; the audit derives the domain and slug from your input.

#Response

Response (trimmed)

{
  "slug": "stripe",
  "domain": "stripe.com",
  "name": "Stripe",
  "scores": {
    "agentready": 74,
    "discovery": 58,
    "understanding": 83,
    "adoption": 88,
    "operability": 67
  },
  "facts": {
    "agent_capability.api": true,
    "agent_capability.mcp_available": true,
    "agent_capability.llms_txt": true,
    "agent_capability.sdks": ["go", "typescript", "ruby", "python", "php", "csharp", "java"],
    "pricing.self_serve": true,
    "pricing.free_tier": false
  },
  "issues": [
    "No OpenAPI spec: publish one so agents can generate a client.",
    "No AGENTS.md skill file: publish one so coding agents get install, config, and usage context.",
    "No markdown mirror: serve text/markdown so agents get clean content, not HTML."
  ],
  "refreshed": {
    "company": ["description", "products", "categories", "technology", "recent_news"],
    "agent_capability": ["api", "cli"]
  },
  "signals": {
    "mcpAvailable": true,
    "cliAvailable": true,
    "apiLikely": true,
    "openapi": false,
    "llmsTxt": true,
    "sdkLanguages": ["go", "typescript", "ruby", "python", "php", "csharp", "java"],
    "npmPackages": ["stripe"],
    "pypiPackages": ["stripe"],
    "robotsAiAllowed": true,
    "sitemap": false,
    "agentsMd": false,
    "githubOrg": "stripe",
    "evidence": ["GitHub org: github.com/stripe", "An MCP server repository exists on GitHub.", "An llms.txt file is served."]
  }
}

#Response fields

scoresobject

agentready plus the four pillar sub-scores (discovery, understanding, adoption, operability), each 0 to 100. See Methodology.

factsobject

A flat map of the scored facts, keyed namespace.key, for example agent_capability.mcp_available. These are the values the score is derived from. See Schemas.

issuesstring[]

The highest-value missing signals, ordered by impact. This is the fix list a vendor works through to raise the score.

signalsobject

The deterministic discovery signals gathered from GitHub, npm, PyPI, llms.txt, and OpenAPI probes, plus an evidence array of human-readable notes. High-confidence facts that do not depend on marketing copy.

refreshedobject

Which fields were re-fetched on this run, grouped by namespace.

Note

An audit spends money: it crawls pages, searches, and runs extraction models. It is metered as one audit_run event. Keyed callers are billed against their plan; anonymous callers fall under the trial limit.

Methodology: how the score is computed.
Registry: browse tools already scored.
Find tools: ranked candidates for a task.

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