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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds: BGE-base-en embeddings, cosine similarity over 500-char windows, 200K char cap with truncation flag, and every passage carries an offset for verification. No contradictions.

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?

Three sentences: first states core purpose, second gives usage context and benefits, third provides technical details (embedding model, window size, character cap). Every sentence is essential and well-structured, front-loaded with key info.

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?

For a tool with 3 parameters, 100% schema coverage, and no output schema, the description explains input parameters well, describes output implicitly (passages with offsets and scores), and adds technical specs. It is nearly complete, though output format could be slightly more explicit.

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. Description adds value by providing usage examples for the query parameter (e.g., 'supply-chain risk') and reinforcing the max length for text. This extra context elevates 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 'Semantic search INSIDE a fetched record,' specifying the verb 'search' and the resource 'record.' It differentiates from siblings by explicitly pairing with 'ask_pipeworx_grounded' and contrasting the approach of fetching and grounding over relevant passages.

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 saves context and returns only relevant passages. Also mentions pairing with ask_pipeworx_grounded for a grounded workflow, providing clear when-to-use guidance.

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