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

With no annotations, the description carries the behavioral burden. It clearly discloses what the call returns and implies a read-only inspection operation. It doesn't discuss auth, failure modes, or output format, but for a retrieval tool the returned contents are the key behavioral information.

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 tight, purposeful sentences. The first front-loads the full output contract with a colon-separated list; the second gives workflow context. There is no repetition 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 3-parameter tool with no output schema, the description is largely complete: it lists the return contents and the schema documents all parameters. Minor gaps, such as output format or invalid app_id handling, are acceptable for a read-oriented dossier tool.

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 description coverage is 100%, so the baseline is 3. The description adds little beyond the schema: it reinforces that the target is one app and that it follows from find_low_hanging_fruit, but app_id, storefront, and include_github are already fully described in 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's action: retrieving a full evidence dossier for one app, and enumerates its contents: score breakdown, diagnosis, complaint themes, verbatim quotes, review provenance, and open-source accelerators. This distinguishes it from broader sibling tools like market_stats or generate_app_brief.

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?

It gives explicit usage context: 'Use after find_low_hanging_fruit to inspect a specific candidate.' This tells an agent when in the workflow to invoke it, though it does not explicitly compare against nearby alternatives like explain_method or generate_app_brief.

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