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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description discloses concrete implementation details: BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, a 200K character cap with truncation flagging, and the return structure (passages with character offsets and similarity scores). This gives the agent a rich behavioral model without contradicting the 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 three sentences long and every sentence adds value. It front-loads the core action, then explains when to use it, and finishes with technical specifics. No fluff or repetition; the structure is logical—purpose, usage context, and behavioral detail. This is an exemplary balance of richness and efficiency.

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 complexity (3 params, 2 required, no output schema), the description fully covers the context: what the tool does, how the input is used, what output to expect (top-N passages with offsets and scores), and edge-case handling (truncation). It also situates the tool within a larger workflow with ask_pipeworx_grounded, making it self-contained for an agent to decide when and how to invoke 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?

The schema already covers all three parameters with descriptions (100% coverage), so the baseline is 3. However, the description adds semantic context beyond the schema: it clarifies that 'text' is 'the text you already pulled' (e.g., a SEC 10-K body) and provides concrete query examples ('supply-chain risk', 'drug interactions with warfarin'). This elevates the parameter semantics from merely descriptive to practically illustrative.

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+resource: 'Semantic search INSIDE a fetched record.' It clearly distinguishes from sibling tools like ask_pipeworx by emphasizing the 'inside a fetched record' scope and by naming the companion tool ask_pipeworx_grounded. This makes the tool's purpose 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 gives explicit guidance: 'Use when the record is too big to cram into the prompt' and explains the benefit ('saves context, returns only the passages that matter'). It also provides an alternative workflow with ask_pipeworx_grounded, showing when to pair this tool rather than use others. This is strong usage guidance beyond just stating the function.

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