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

Goes well beyond the annotations: names the four possible statuses, explains that product is populated on completion and error on scrape failure, and clarifies that a failure still returns HTTP 200 (not a transport error) with a structured error payload including retry_recommended and retry_after_seconds. This is exactly the behavioral context annotations cannot carry.

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?

Front-loaded with the core action, then lifecycle and error details in compact sentences. The final sentence partially restates the earlier 'error is populated' clause, a small redundancy.

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?

An output schema exists so return structure is already documented, and the description nonetheless covers the polling lifecycle and error semantics thoroughly. The remaining gap is polling cadence/backoff guidance, only indirectly signalled by retry_after_seconds.

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?

A single parameter with 100% schema description coverage, so the schema already carries the semantics. The description restates 'by job_id' and adds no format or sourcing detail beyond what the schema note provides.

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?

States a specific verb and resource: 'Poll an async tracking job by job_id', and enumerates the statuses it returns. An agent can tell it is a job-status lookup, though it doesn't explicitly contrast with the similarly named sibling get_watch_status.

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?

'Poll' implies repeated calls and the schema note 'Job ID from track_product' hints at the calling sequence, but the description never says when to call this versus get_watch_status or how often to poll. Usage is implied rather than stated.

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