MCP Readiness Auditor

Store Positioning

Store title: MCP Readiness Auditor

Short description: Audit tools/actors for MCP and AI-agent readiness: schemas, docs, examples, tasks, and outputs.

SEO title: MCP Readiness Auditor — API, schema, and developer QA

SEO description: Audit tools/actors for MCP and AI-agent readiness: schemas, docs, examples, tasks, and outputs. Use it to catch contract drift, schema mistakes, unsafe endpoint assumptions, and developer-tool quality issues before release.

Categories: SEO_TOOLS, DEVELOPER_TOOLS

Keywords: mcp, readiness, auditor, schema, api/developer qa

Fixed-Inclusive PPE Pricing

This actor uses pay-per-event pricing. Event prices include Apify platform usage; users are not expected to pay a separate platform-usage pass-through charge for the configured pricing model.

• Tier: A1 — API/developer QA
• Primary event: schema-validated at $0.00650 base
• Default max charge: $10.00
• Store discounts: FREE/BRONZE base, SILVER discounted, GOLD deepest approved discount

Event set:

• actor-start: base $0.00500, GOLD $0.00400. MCP Readiness Auditor: charged when actor start is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• schema-validated: base $0.00650, GOLD $0.00520. MCP Readiness Auditor: charged when schema validated is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• contract-rule-checked: base $0.00390, GOLD $0.00312. MCP Readiness Auditor: charged when contract rule checked is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• report-generated: base $0.05000, GOLD $0.04000. MCP Readiness Auditor: charged when report generated is completed. The price includes Apify platform usage; no separate usage pass-through is intended.

Public Task Concepts

• Validate MCP Readiness Auditor before release
• Run a bounded MCP Readiness Auditor QA check
• Generate developer QA evidence for MCP Readiness Auditor
• Extract evidence-backed mcp rows for client QA
• Prioritize mcp fixes before a launch or migration

Audit tools/actors for MCP and AI-agent readiness: schemas, docs, examples, tasks, and outputs.

This Actor is a premium Apify tool for the use case described below. It is designed for controlled runs with capped inputs, transparent pricing, and evidence-backed outputs.

What It Does

MCP Readiness Auditor turns supplied source evidence into actor-specific dataset rows for ai-agent tooling workflows. It uses deterministic parsing, strict caps, supplied examples, and top-level fields that match the dataset schema.

What It Does Not Do

• It does not claim outcomes beyond the supplied public evidence and configured run limits.
• It does not fetch URLs unless fetchUrls is explicitly enabled and the URLs are public sources you are allowed to process.
• It does not use unsupported AI claims or hidden enrichment.
• It does not replace legal, financial, medical, or compliance review.

Input Fields

• targets: Source rows, snapshots, text, HTML, records, schemas, logs, or workflow details.
• fetchUrls: Defaults to false so you can validate supplied snapshots first. Enable it when you want the Actor to fetch public URLs within configured limits.
• includeReport: Writes JSON and Markdown artifacts when enabled.
• maxTargets: Conservative cap for small, predictable runs.
• maxTextBytes: Per-target text cap.

Example Input

{
  "targets": [
    {
      "sourceId": "actor-ready-for-mcp",
      "actorName": "dataset-qa-auditor",
      "text": "README includes input schema, output schema, dataset schema, example input, example output, public task examples, limitations, and troubleshooting.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "datasetId": {
            "type": "string",
            "description": "Dataset ID to audit.",
            "default": "sample"
          }
        }
      }
    },
    {
      "sourceId": "actor-needs-mcp-work",
      "actorName": "thin-actor",
      "text": "README says run the actor and check results. Missing output schema and examples.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "query": {
            "type": "string"
          }
        }
      }
    }
  ],
  "fetchUrls": false,
  "includeReport": true,
  "maxTargets": 2
}

Output Fields

• rowId
• sourceId
• sourceUrl
• sourceType
• status
• severity
• score
• summary
• recommendation
• checkedAt
• pricingTemplate
• pricingEventName
• toolName
• inputSchemaStatus
• outputSchemaStatus
• descriptionQuality
• exampleCoverage
• authDisclosure
• errorHandlingDisclosure
• agentReadinessScore
• fixHint

Example Output

[
  {
    "rowId": "mcp-readiness-auditor_d6fedac65cd6f1a3",
    "sourceId": "actor-ready-for-mcp",
    "sourceUrl": null,
    "sourceType": "inline",
    "status": "ok",
    "severity": "low",
    "score": 92,
    "summary": "MCP Readiness Auditor produced actor-specific row 1 for actor-ready-for-mcp.",
    "recommendation": "Ready for agent tool catalog review.",
    "checkedAt": "2026-07-02T00:00:00.000Z",
    "pricingTemplate": "A1 — API/developer QA",
    "pricingEventName": "schema-validated",
    "toolName": "dataset-qa-auditor",
    "inputSchemaStatus": "documented",
    "outputSchemaStatus": "documented",
    "descriptionQuality": "specific",
    "exampleCoverage": "examples-present",
    "authDisclosure": "not-disclosed",
    "errorHandlingDisclosure": "not-documented",
    "agentReadinessScore": 80,
    "fixHint": "Ready for agent tool catalog review."
  },
  {
    "rowId": "mcp-readiness-auditor_8f2b1480fd4cd13e",
    "sourceId": "actor-needs-mcp-work",
    "sourceUrl": null,
    "sourceType": "inline",
    "status": "ok",
    "severity": "low",
    "score": 92,
    "summary": "MCP Readiness Auditor produced actor-specific row 1 for actor-needs-mcp-work.",
    "recommendation": "Ready for agent tool catalog review.",
    "checkedAt": "2026-07-02T00:00:00.000Z",
    "pricingTemplate": "A1 — API/developer QA",
    "pricingEventName": "schema-validated",
    "toolName": "thin-actor",
    "inputSchemaStatus": "documented",
    "outputSchemaStatus": "documented",
    "descriptionQuality": "thin",
    "exampleCoverage": "examples-present",
    "authDisclosure": "not-disclosed",
    "errorHandlingDisclosure": "not-documented",
    "agentReadinessScore": 80,
    "fixHint": "Ready for agent tool catalog review."
  }
]

Public Task Concepts

Example tasks are included for common use cases and can be published as Apify public tasks after review/approval.

Limitations

Use capped runs first, enable URL fetching only for public URLs you are allowed to process, and review evidence before using results in operational decisions. Store icon upload, public task publication, and live metadata changes are handled through the approved release process.

FAQ

Is this ready for scaled runs? Start with the capped example tasks, confirm the evidence quality, then increase limits deliberately.

How are costs controlled? Costs are controlled with input caps and max-charge handling. See the pricing section for event prices and cost controls.