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

Annotations already mark this as read-only and idempotent; the description adds valuable behavioral context: matching is performed against trigger text, compact hits are returned, and each hit quotes the matching line. No contradiction with annotations.

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?

Three sentences, front-loaded with the tool's scope and matching behavior, then the return shape, then the routing alternative. No filler or repetition of schema details.

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?

The description covers what is searched, how matching works, what results look like, and when to use the sibling instead. Given annotations, a rich input schema, and an output schema, nothing essential is missing for an agent to select and invoke the tool correctly.

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%, so the baseline is 3. The description adds value by explaining why plain task language works (trigger text matching) and by providing query examples that map to the query parameter, going slightly beyond the schema's own descriptions.

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 states a specific verb and resource: search over 51 skills and 14 journeys by keyword or phrase. It also supplies concrete examples ('my UI looks amateur', 'pricing', 'dark mode') and clearly differentiates itself from sibling tools like recommend_skills.

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 explicitly tells the agent when to prefer recommend_skills ('when you have a task rather than a keyword'), which is the key routing decision among siblings. The trigger-text matching explanation further clarifies appropriate query language.

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