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Check AI Crawl Status

check_ai_crawl_status
Read-only

Check the status of an AI crawl job. Use this to see if a crawl has completed and inform the user when tests are ready.

Input Schema

NameRequiredDescriptionDefault
contextNoExplain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): "Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization."
crawl_idYesThe crawl job ID to check status for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered and the description's job is to add context beyond that. It adds the purpose of polling for completion and the expectation of informing the user, but says nothing about polling cadence, the possible status values, or whether a completed state carries the results payload.

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

Conciseness4/5

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

Two short sentences, front-loaded with the operation and followed by the usage cue. No filler; it could only be improved by spending a few more words on state values or the results sibling.

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

Completeness2/5

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

There is no output schema, so the description is the only place an agent can learn what a status response contains or how completion is signalled, and it does not say. For a polling tool in a large sibling set with a required context parameter, the definition leaves the return-value and next-step story incomplete.

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%, so both crawl_id and the mandated context parameter are fully documented in the schema, including the 15-25 word and third-person constraints. The description adds no semantics beyond that, which is the expected baseline when the schema does the work.

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

Purpose4/5

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

Specific verb+resource ("Check the status of an AI crawl job"), which is clear enough to act on. However, it never distinguishes itself from close siblings like get_ai_crawl_results, list_ai_crawl_jobs, or check_generation_status, so an agent must infer which one fetches actual results versus just polling state.

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

Usage Guidelines3/5

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

"Use this to see if a crawl has completed" gives an implied trigger, but there is no guidance on where crawl_id comes from (start_ai_crawl vs list_ai_crawl_jobs) nor on what to do after completion (get_ai_crawl_results). The when-to-use is present; the when-not-to-use and the next-step routing are absent.

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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