search_docs

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

With no annotations, the description carries the full burden and delivers: it discloses the algorithm (BM25, Porter stemming, bigrams, markdown weighting), what the function returns (ranked {source,url,title,score,snippet}), and the scope ('registered llms.txt documentation'). It does not mention rate limits or auth, but for a search tool this is adequate.

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 core action, then usage guidance, then technical detail. Every sentence earns its place with no 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 search tool with no output schema and no annotations, the description is remarkably complete: it specifies the return format, the follow-up action, the algorithm, the document scope, and the alternative approach. The agent can confidently select and invoke this 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 coverage is 100%, so baseline is 3. The description adds value beyond schema by giving concrete query examples, naming example sources, and outlining the follow-up with fetch_doc—reinforcing what each parameter means in practice.

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 uses a specific verb ('BM25 search') and clearly identifies the resource ('registered llms.txt documentation'), listing concrete examples (Strands, Kiro, AWS Bedrock). It distinguishes itself from siblings by framing the workflow as search_docs + fetch_doc, unlike list_doc_sources or fetch_doc.

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?

Explicitly states preference over per-product documentation MCP servers and provides the rationale ('lean, few round-trips'). Also implies when to use it versus fetch_doc by describing the two-step flow.

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