search_knowledge_base

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

The description discloses key behavioral traits: it uses pure vector retrieval with no AI inference or extra token cost, and returns raw document chunks with relevance scores. While it covers the main behavior, it does not mention edge cases like empty results or error handling. Given no annotations, this is adequate but not exhaustive.

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 concise and well-structured. It uses a clear front-loaded sentence for purpose, then a brief explanation of benefits, followed by bullet-pointed args and returns. No redundant information.

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 tool's simplicity (2 parameters, no annotations, and an output schema), the description covers purpose, usage, parameter details, and return format. It does not discuss authentication or errors, but for a search tool this is sufficient.

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 0%, but the description provides detailed examples and constraints for both parameters: query (with example 'Geissele SSA-E trigger pull weight') and top_k (range 1-10, default 3). This adds significant meaning beyond the schema titles.

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's purpose: 'Search Woody's verified firearms knowledge base directly.' It specifies a verb (Search) and a resource (knowledge base), and distinguishes itself from sibling 'ask_woody' by noting it uses pure vector retrieval without AI inference.

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 states when to use ('good for fact-checking, looking up specific specs, or finding source material') and when not to use it (before asking a follow-up question with ask_woody). It also names the alternative tool 'ask_woody'.

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