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

Annotations provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant behavioral details: BGE-base-en embeddings, cosine similarity over 500-char windows, 200K char limit with truncation flag, and character offsets for verification. No contradiction 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?

Approximately 5 sentences, front-loaded with the core purpose, then usage, then technical details. Every sentence adds value with no redundancy or 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?

All aspects are covered: input (text, query, limit), output (passages, offsets, scores), constraints (200K chars, max 20), mechanics (BGE, cosine, windows), and integration pattern (pairing with ask_pipeworx_grounded). No gaps.

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%, baseline 3. The description adds value with examples for 'text' (e.g., SEC 10-K body) and 'query' (e.g., 'supply-chain risk'), and clarifies output behavior (passages with offsets and scores). However, it does not add new parameter semantics beyond what the schema provides.

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 the tool performs 'Semantic search INSIDE a fetched record', specifying the verb (search), resource (a fetched record/text), and scope. It distinguishes from siblings like 'ask_pipeworx_grounded' by mentioning pairing and from general search tools by emphasizing it works on already-fetched text.

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?

Explicit when-to-use guidance: 'Use when the record is too big to cram into the prompt — search_within saves context...' It also mentions pairing with ask_pipeworx_grounded and provides examples of queries. No explicit 'when not', but the context is clear.

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