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

The description adds substantial behavioral detail beyond the annotations: it specifies the return format ('top-N passages with character offsets and similarity scores'), the embedding model and matching algorithm ('BGE-base-en embeddings + cosine over 500-char overlapping windows'), and the input cap with truncation behavior ('cap is 200K chars (longer inputs are truncated and flagged)'). Annotations are consistent; 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 front-loaded with the core purpose, then delivers use case, workflow, technical details, and limits in a compact structure. Every sentence contributes distinct information: purpose, input/output, when to use, sibling relationship, and operational constraints. No filler or 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?

Even without an output schema, the description explains what the agent receives (passages with offsets and similarity scores) and how results can be used ('verify a verbatim quote'). It covers inputs, limits, truncation, and the complementary workflow, making the tool's behavior fully understandable for an agent selecting and invoking it.

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%, with each parameter (text, query, limit) already described with examples and defaults. The description reinforces this by calling text 'the text you already pulled' and query 'natural-language query,' but it does not add meaning beyond what the schema provides. The baseline of 3 is appropriate because the schema carries the parameter documentation burden.

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 immediately identifies the tool as 'Semantic search INSIDE a fetched record' with a specific verb and resource, and it distinguishes itself from siblings by describing the workflow with ask_pipeworx_grounded: 'fetch with the gateway, ground over the relevant passages instead of the whole document.' This makes the tool's scope and differentiation clear.

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 gives explicit when-to-use guidance: 'Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter.' It also names a complementary sibling tool (ask_pipeworx_grounded) and explains how the two fit together, which provides clear context for selecting between tools.

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