search_docs

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

The description discloses important behavioral nuance, such as min_score applying only to the vector search leg and that keyword/BM25 matches may surface regardless, which provides context beyond the schema.

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 a clear first sentence followed by a compact Args section; each item is informative and necessary.

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 five parameters and no annotations, the description thoroughly explains all inputs, includes examples, and notes edge-case behavior, making it sufficient given the output schema.

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?

Since schema description coverage is 0%, the description compensates by explaining each parameter's purpose, defaults, and allowed values (e.g., source_type 'devdocs' or 'local'), including caveats for min_score.

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 first sentence, 'Search documentation using semantic similarity,' clearly identifies the verb (search), resource (documentation), and method (semantic similarity), distinguishing it from sibling tools like list_docs or get_document.

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 implies usage for natural language queries but does not explicitly state when to prefer this over sibling tools like get_document or list_docs, nor does it provide exclusions or alternatives.

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