search_documentation

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

With no annotations, the description carries the full burden. It discloses: uses RAG with vector retrieval, two backends chosen automatically, lazy index building with disk caching, and auto-rebuild on source changes. This is detailed, though it omits potential rate limits or performance constraints.

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 well-structured with sections for overview, backend info, and parameter details. It is slightly verbose but each part adds value. Could be reduced by removing the 'pip install' hint or internal 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?

With an output schema available, the description completes the picture by specifying return structure (ranked results with text, source, score, chunk index). It covers most necessary context for a search tool, though missing notes on result limits or error handling.

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 provides 0% description coverage, but the description explains each parameter (query, top_k, force_reindex) with clear purpose and defaults. This fully compensates, adding meaning beyond the raw 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 performs semantic search across all machine manuals and bearing catalogs, using specific verb 'search' and broad resource scope. While it distinguishes from many sibling tools by its breadth, it does not explicitly differentiate from closely related tools like search_bearing_catalog or read_manual_excerpt.

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 explains the underlying search backends (FAISS, TF-IDF) but provides no explicit guidance on when to use this tool versus alternatives. An agent would need to infer context from the broad scope; no exclusions or when-not-to-use are mentioned.

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