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

Annotations already declare the tool is read-only, idempotent, and non-destructive, but the description goes well beyond this by disclosing the embedding model (BGE-base-en), similarity metric (cosine), windowing strategy (500-char overlapping windows), character cap (200K), and truncation behavior (flagged). It also specifies that results include character offsets and similarity scores, giving the agent a clear model of expected behavior.

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 expands into usage guidance, pairing with a sibling, and technical details. Every sentence adds distinct value—use case, return value, workflow, model, cap—without fluff 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?

Given there is no output schema, the description compensates by explaining exactly what the agent gets back (passages, character offsets, similarity scores) and how the tool behaves under size limits. It also covers the workflow relationship with ask_pipeworx_grounded, making it sufficiently 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% for all three parameters, so the schema already explains text, query, and limit. The description adds context by clarifying 'text' means already-pulled fetched content, 'query' is a natural-language request with examples, and the cap behavior. This enriches the schema without repeating it verbatim.

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 ('semantic search') and clearly identifies the resource ('inside a fetched record'), distinguishing it from siblings like search_articles by emphasizing it operates on already-pulled text rather than external sources. It also states the return format (top-N passages with offsets and scores), making the purpose unambiguous.

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 explains the benefit (saves context, returns only relevant passages). It mentions a sibling tool (ask_pipeworx_grounded) as a paired workflow, but does not explicitly state when not to use it or list direct alternatives as preferred in some cases.

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