search_datasheets

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the baseline is covered. The description adds important behavioral context beyond annotations: the tool is limited to previously extracted datasheets, and it performs semantic (natural language) matching rather than exact keyword search. This helps the agent understand scope and result variability. No contradictions 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?

Four sentences, all purposeful. The first sentence states the core function, the second expands on query types, the third gives usage context with an example, and the fourth provides a caveat and alternative. No redundancy, front-loaded, and well-structured.

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 straightforward search tool with comprehensive annotations and schema, the description covers the key aspects: what it searches, the extraction-status limitation, and the differentiation from search_parts. It does not mention the optional 'section_type' parameter, but the schema already fully documents that parameter. Given no output schema, the description is sufficiently complete for an agent to decide when and how to invoke it.

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 baseline is 3. The description adds value by clarifying what the 'query' parameter expects: natural language about specifications, features, or capabilities, with an example ('low-noise LDO with PSRR above 70dB'). It does not repeat parameter descriptions from the schema but provides richer guidance on how to phrase queries.

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 opens with a specific verb+resource: 'Semantic search across all extracted datasheets.' It clearly states the tool finds components matching natural language queries about specifications, features, or capabilities. It also distinguishes from siblings by explicitly directing users to 'search_parts' for part-number lookups.

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 explicit when-to-use guidance: 'Best for broad spec-based discovery across all parts' and gives a concrete example. It also includes a clear exclusion and alternative: 'Only searches datasheets that have been previously extracted — not all parts that exist. For finding specific parts by number, use search_parts instead.' This fully covers usage context.

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