Queue Document Analysis

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

Annotations are present (readOnlyHint=false, etc.) and the description adds behavioral context: execution is non-blocking, returns immediately with a job ID, and runs in the background. No contradictions with annotations. Minor omission: doesn't mention that the job might fail or require authentication, but overall good.

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

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

Two concise sentences that front-load the core purpose and follow with usage guidance. Every sentence adds value; no filler.

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

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

Given the presence of an output schema (job ID) and annotations, the description covers everything needed: what it does, when to use, how to monitor progress, and the asynchronous nature. Fully sufficient for correct tool selection and invocation.

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

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

Schema coverage is 100%. The description adds value by explaining that documentId comes from get_structure and that content may be empty. The options are already described in the schema. The parameter overview is adequate but not exceptional beyond the schema.

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

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

The description clearly states the tool enqueues a background NLP analysis (readability, entities, sentiment) on a single document and returns a job ID. It explicitly distinguishes from the synchronous sibling 'analyze_document' by mentioning blocking vs. non-blocking behavior.

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

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

The description explicitly states when to use this tool (large documents where synchronous would be slow) and when to use the alternative 'analyze_document' for quick inline results. It also names related tools for polling (get_job_status) and cancellation (cancel_job).

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