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

Annotations already declare the tool read-only, open-world, idempotent, and non-destructive. The description goes beyond these by disclosing the embedding model (BGE-base-en), similarity scoring (cosine), windowing (500-char overlapping windows), input cap (200K chars), and truncation behavior (longer inputs are truncated and flagged). These details give the agent an accurate mental model of the tool's behavior without contradicting any annotation.

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 three sentences, each earning its place: purpose, usage guidance, and technical behavior. It front-loads the core action and uses capitalization/dashes for emphasis. No fluff, all high-signal content.

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, no output schema, and rich annotations, the description fully covers what the tool does, when to use it, how it behaves, what parameters mean, and what the return looks like (offsets and similarity scores). It also addresses edge cases (truncation) and integration with a sibling tool. This is a complete and self-sufficient description.

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 description coverage is 100%, so the baseline is 3. The description enriches this by explaining the intended use of 'text' (passed from a fetched record, e.g., SEC 10-K body, article), clarifying that 'query' is natural-language with concrete examples, and specifying that 'limit' controls top-N passages. This adds practical meaning beyond the schema's field 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 opens with a specific verb and resource: 'Semantic search INSIDE a fetched record.' It clearly distinguishes this tool from siblings by emphasizing that it operates on already-fetched text rather than external sources, and it names the output (top-N passages with offsets and scores). This is unambiguous and well-differentiated.

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 states when to use the tool ('when the record is too big to cram into the prompt'), explains the benefit ('saves context, returns only the passages that matter'), and directly names an alternative/sibling ('Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document'). This provides clear and actionable usage guidance with an explicit alternative.

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