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SEO Score API

by io.github.SeoScoreAPI
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Start Deep Audit

start_deep_audit

Queue a Deep Audit and return immediately with a job_id. Poll it with get_deep_audit. Same plans as deep_audit.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "start_deep_audit"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesURL to audit. Must include the protocol (https://...).
webhook_urlNoOptional public URL POSTed once when the job completes.
business_typeNoOptional. Tunes which checks apply; worked out from the page when omitted.

Output Schema

NameRequiredDescriptionDefault
pollNoPath to poll for status.
job_idNoJob id to poll.
statusNoqueued, running, completed or failed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false and destructiveHint=false, and the description adds the essential asynchronous contract (immediate return, job_id, poll via get_deep_audit) that annotations cannot convey. It stays silent on non-idempotency (each call queues a new job) and on any rate or quota limits, which are the remaining gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with the action and return behavior, then the follow-up tool, then the plan note. No padding or repeated information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, return shape needs no explanation, and the description still surfaces the one return value that matters for next steps (job_id). It could note that webhook_url makes polling optional, but nothing needed to invoke the tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with url, webhook_url, and business_type all documented including the enum, so the schema carries the parameter burden and baseline 3 applies. The description adds no parameter-level meaning such as URL normalization rules or when to prefer webhook_url over polling.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Queue a Deep Audit') plus the key behavioral trait ('return immediately with a job_id'), which cleanly separates it from the synchronous deep_audit and from get_deep_audit whose job is polling. An agent can pick the right tool from the description alone.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes the agent to get_deep_audit for polling, which is the main follow-up decision. It does not, however, say when to choose this queued variant over the sibling deep_audit; 'Same plans as deep_audit' is a plan-compatibility note rather than selection guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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