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

The description adds significant behavioral context beyond the annotations: it reveals the use of BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, a 200K char limit with truncation flagging, and that results include character offsets for verification. This fully informs the agent about operational details.

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 two focused paragraphs: the first sentence defines the primary action, the second explains the use case, and the third provides technical details. Every sentence contributes unique information without redundancy or fluff.

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?

Despite lacking an output schema, the description fully explains what the tool returns (top-N passages with offsets and scores) and its technical limits. It also covers usage context and pairing with a sibling tool, making it self-contained for a tool with three parameters.

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 providing concrete examples for 'text' (SEC 10-K, article) and 'query' (supply-chain risk, revenue queries), and clarifies the natural-language nature. This moves it above baseline.

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 inside a fetched record, specifying the verb 'search', the resource 'fetched record', and the scope. It distinguishes from siblings by contrasting with full-document tools and explicitly mentioning the pairing with ask_pipeworx_grounded.

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 advises when to use the tool: when the record is too large for the prompt, and it recommends pairing with ask_pipeworx_grounded for grounding. This provides clear context and alternatives, exceeding minimal guidance.

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