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

The annotations already declare readOnly/idempotent, but the description adds algorithmic specifics (BGE-base-en embeddings, cosine, 500-char overlapping windows) and the 200K-char truncation behavior with a flag. It also states that results include offsets for verifying verbatim quotes.

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?

Five meaningful sentences that front-load purpose, then workflow, algorithm, and constraints. No filler; every detail serves a purpose.

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?

No output schema exists, so the description carries the burden of explaining returns; it does so explicitly (passages, offsets, similarity scores). It covers input constraints, algorithm, pairing, and use case, fully equipping an agent.

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 enriches the 'text' parameter by giving real-world examples (SEC 10-K, article, long tool result) and clarifies how 'query' should be phrased. It also ties 'limit' to 'top-N passages'.

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 details inputs (text, query) and outputs (top-N passages, character offsets, similarity scores), distinguishing itself from siblings by explicitly comparing with ask_pipeworx_grounded.

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?

Explicitly states when to use: 'Use when the record is too big to cram into the prompt — search_within saves context.' It also names a complementary sibling (ask_pipeworx_grounded) and explains the workflow: fetch with the gateway, ground over relevant passages.

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