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

Annotations already declare the tool read-only and non-destructive, so the safety profile is covered. The description adds that the tool works via AI vector similarity, but doesn't elaborate on ordering, scoring, or variability of results. That's solid but not deep context.

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; the first identifies the core action and mechanism, the second adds a practical user-facing use case. There is no redundancy or 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?

For a small, read-only tool with two well-documented parameters, the description plus schema is almost complete. The only gap is the lack of an explicit statement of the return shape, and since there is no output schema, the description might have clarified it returns a list of similar job listings.

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?

The input schema already covers both parameters with useful descriptions, including an example job handle for job_id and a default for per_page. The description itself adds no parameter-specific semantics, so it stays at the baseline for full schema coverage.

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?

Description clearly states it 'finds jobs similar to a given job listing' and the method 'AI vector similarity' plus the use-case 'more like this' discovery. This differentiates it from sibling tools like search_jobs (query-based) and get_job (exact retrieval).

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 provides clear context for when it is appropriate: when there is already a specific job listing and the request is to discover similar jobs. It doesn't explicitly name alternatives or state when not to use it, so it misses the top score.

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