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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it is fast, semantic, returns ranked matches with similarity scores, and does not use LLM scoring. It also implies lower cost. These details go beyond a generic statement and help the agent anticipate output and tradeoffs, though it does not cover potential errors or limits.

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?

The description is three short sentences, each contributing essential information: what it is, what it returns, and when to use it over an alternative. It is front-loaded with the core function and contains no filler or redundant phrasing.

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 moderate complexity (4 params, 1 required, no output schema), the description covers the return value ('ranked matches with similarity scores'), the corpus size, and the speed/cost tradeoff. It could be more detailed about the exact output format (e.g., fields per match), but it provides sufficient context for an agent to decide and invoke 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?

The input schema has 100% coverage with meaningful descriptions for all four parameters, so the baseline is 3. The description does not add new parameter-level details beyond what the schema provides, but it does reinforce the main use case (code search) without redundancy. It neither elevates nor diminishes the schema's clarity.

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 a specific verb ('search') and a specific resource ('the 2M+ prior-art corpus'), and distinguishes the tool from siblings by noting 'without LLM scoring' and implying a different cost profile than precheck_score. This is a strong, differentiated purpose.

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 recommends this tool over precheck_score ('Cheaper than precheck_score') and specifies the condition ('when you only need references'). While it does not explicitly list exclusions or other alternatives, the primary alternative is clearly identified, giving actionable guidance.

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