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

Beyond the annotations (readOnly, idempotent, etc.), the description discloses substantial behavioral details: embedding model (BGE-base-en), cosine similarity over 500-char overlapping windows, a 200K character cap, and truncation with a flag. It also reveals that returned passages include character offsets and similarity scores, enriching the agent's understanding of expected behavior.

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 and front-loaded with the core purpose, then expands into usage context, pairing, and technical details. Every sentence adds value, and the length is justified by the tool's complexity. No fluff or redundancy.

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 moderate complexity and the absence of an output schema, the description is remarkably complete. It explains inputs, outputs, use case, limits, and technical behavior. The agent has enough context to select and invoke the tool correctly without needing additional clarification.

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%, and the schema already provides good descriptions for text, query, and limit. The description adds nuance by clarifying that the text is something 'you already pulled' (e.g., a SEC 10-K body), reinforces the natural-language nature of the query, and mentions the output format with offsets/scores, which indirectly clarifies the purpose of the parameters. This goes beyond the baseline but does not dramatically change parameter understanding.

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 identifies the tool as semantic search inside an already-fetched record, specifying inputs (text + query) and outputs (top-N passages with offsets and scores). It distinguishes itself from the sibling 'search' tool and 'ask_pipeworx_grounded' by emphasizing the pre-fetched text requirement and passage-level retrieval.

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 when to use: 'Use when the record is too big to cram into the prompt...' and provides a concrete pairing with ask_pipeworx_grounded, explaining the workflow of fetching first and then grounding over relevant passages. This gives clear context and an alternative to consider.

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