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

Beyond the annotations (readOnly, idempotent, non-destructive), the description reveals concrete behavioral details: the embedding model (BGE-base-en), similarity metric (cosine), windowing strategy (500-char overlapping windows), output characteristics (character offsets and similarity scores), and truncation behavior with a 200K char cap and flag. This far exceeds annotation coverage.

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 dense but well-structured: it opens with the core action, states the use case, explains the value proposition with offsets, gives a sibling-tool pairing, and closes with technical specifics. Every sentence contributes unique information with no filler.

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 fully explains what the agent will receive (top-N passages, character offsets, similarity scores). It covers input limits, truncation, algorithm, and intended workflow, making it complete for an agent to select and invoke the tool correctly.

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 extra meaning by explaining how 'text' and 'query' interact ('pass the text you already pulled plus a natural-language query'), providing concrete query examples, and giving behavioral context like the 500-char windows and top-N results. This enriches parameter 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 uses a specific verb and resource ('Semantic search INSIDE a fetched record'), clearly distinguishing it from sibling tools like ask_pipeworx_grounded by focusing on searching within already-fetched text. It also states the exact scope (top-N passages with offsets and scores), leaving no ambiguity.

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 states when to use the tool ('Use when the record is too big to cram into the prompt') and provides a clear pairing with ask_pipeworx_grounded, explaining the workflow: fetch with the gateway, ground over relevant passages. This is excellent guidance for tool selection.

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