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

The description adds significant behavioral details not covered by annotations: embedding model (BGE-base-en), window size (500-char overlapping), character cap (200K chars), truncation behavior, and return fields (passages, offsets, similarity scores). No contradictions with annotations.

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 well-structured, front-loading the core action and then providing technical details. It is detailed but not overly verbose, though minor trimming could improve conciseness.

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?

Despite no output schema, the description covers return value semantics (passages, offsets, similarity scores). It also includes embedding and size limit details, and suggests complementary tools. This is comprehensive for a 3-parameter tool without output schema.

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%, and the description adds value for each parameter: 'text' gains max size, 'limit' gains range and default, 'query' gains examples and clarification of natural-language query. This enhances understanding beyond the 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 it performs semantic search inside a fetched record, specifying the inputs (text and query) and the output (top-N passages with offsets and scores). It distinguishes itself from sibling tools by mentioning pairing with ask_pipeworx_grounded and highlighting the benefit of saving context.

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 suggests pairing with ask_pipeworx_grounded as an alternative workflow. It does not explicitly list when not to use, but the guidance is clear enough.

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