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

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds specific behavioral details: uses BGE-base-en embeddings with cosine similarity over 500-char overlapping windows, a 200K char cap with truncation and flagging, and returns passages with offsets and scores. No contradiction.

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 about 100 words, front-loaded with the core purpose, and each sentence adds essential information. No redundant or extraneous 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?

No output schema, but the description details the return format (passages with offsets and similarity scores), explains the embedding and windowing approach, and specifies input limits. This is comprehensive for a retrieval tool.

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 enhances understanding by explaining the text parameter as something the agent already pulled, giving natural-language query examples, and clarifying the limit's range. This adds value beyond schema.

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, distinguishing it from siblings like ask_pipeworx by specifying it is for large records that are too big to fit in the prompt. Examples of use cases are provided.

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 tells when to use this tool: 'when the record is too big to cram into the prompt.' It also pairs it with ask_pipeworx_grounded for grounding over passages, providing clear alternatives and context.

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