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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, but the description adds valuable behavioral context: it returns top-N passages with character offsets and similarity scores, uses BGE-base-en embeddings with cosine over 500-char windows, and truncates inputs over 200K chars with a flag. This goes well beyond the annotation safety profile and sets accurate expectations for outputs and limits.

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 dense but well-organized: a one-sentence core purpose, followed by usage guidance and then technical details. Every sentence earns its place—no repetition or filler. It front-loads the key verb and resource, making it scannable.

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, full schema coverage, and no output schema, the description covers the essentials: inputs, output characteristics (top-N passages with offsets/scores), algorithm, constraints, and relationship to a sibling tool. It also includes a practical cap and truncation behavior, making it actionable for an AI agent.

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 baseline is 3. The description enhances parameter meaning by showing concrete examples ('SEC 10-K body, an article, a long tool result', 'supply-chain risk') and clarifying that text is the already-pulled content and query is a natural-language request. This adds context beyond the schema's field descriptions, warranting a 4.

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 opens with 'Semantic search INSIDE a fetched record,' which clearly identifies a specific verb (search), resource (fetched record), and scope (inside rather than across records). It distinguishes from sibling tools like ask_pipeworx_grounded by explaining that search_within operates on text already pulled, not on the broader knowledge base.

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?

It provides explicit usage guidance: 'Use when the record is too big to cram into the prompt' and 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document.' This tells the agent exactly when to use it and how it fits with an alternative tool.

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