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

Annotations already document non-destructive, read-only, idempotent, open-world behavior. The description adds specific technical details such as embedding model (BGE-base-en), cosine similarity, 500-char overlapping windows, and a 200K char cap with truncation flag.

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 concise, front-loaded with the core purpose, and every sentence earns its place by adding useful information without 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?

Without an output schema, the description adequately explains return format (top-N passages with character offsets and similarity scores). It also covers constraints like the character cap and truncation behavior. Combined with rich annotations, the tool is fully specified.

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. The description adds value by explaining that the limit parameter controls top-N passages, provides query examples, and clarifies the text parameter's maximum length constraint.

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 that the tool performs semantic search inside a fetched record using a natural-language query, and distinguishes it from siblings by mentioning it pairs with ask_pipeworx_grounded for grounding over 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 recommends using this tool when the record is too large for the prompt, explaining it saves context and returns only relevant passages. Also provides a pairing strategy with another tool.

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