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

Beyond the readOnly/idempotent annotations, the description discloses the truncation behavior with a 200K character cap and flagging, explains the embedding/window approach (BGE-base-en, 500-char windows), and notes that every passage carries an offset for verification. No contradiction with annotations.

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 longer than strictly necessary, but every sentence adds valuable context: purpose, use case, technical implementation, and limits. It is front-loaded with the key purpose and organized logically.

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?

Without an output schema, the description explains the return values (passages, offsets, similarity scores) and the truncation flag. It also covers selection criteria and integration with a sibling tool, making it complete for the agent's needs.

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 the schema already documents all parameters. The description adds some behavioral context (truncation, windowing) but does not significantly extend the meaning of the parameters beyond the schema's own examples and descriptions.

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 does semantic search inside a fetched record, with specific examples of inputs and outputs. It distinguishes itself from siblings like ask_pipeworx_grounded by explicitly focusing on a subset of already-retrieved text.

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 says 'Use when the record is too big to cram into the prompt' and explains how it pairs with ask_pipeworx_grounded, providing clear when-to-use guidance and naming an alternative/complement.

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