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

Beyond the annotations (readOnly, openWorld, idempotent), the description reveals significant behavioral traits: uses 'BGE-base-en embeddings + cosine over 500-char overlapping windows,' enforces a 'cap is 200K chars' with truncation and flagging, and describes output as 'passages with character offsets and similarity scores.' This is exactly the kind of operational detail annotations do not capture.

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 in the first sentence, then methodically covers usage, pairing, and technical details. Each of the five sentences earns its place — no filler. It is slightly longer than the ideal two-sentence example, but the complexity of the tool justifies the extra sentences.

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 no output schema, the description adequately explains return values ('top-N passages with character offsets and similarity scores'), operational limits (200K cap, truncation flag), and the integration workflow with ask_pipeworx_grounded. It covers all essential aspects a caller needs to know: when to use, how it works, what it returns, and its constraints.

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 the baseline is 3. The description adds value by giving real-world examples for both text and query: 'a SEC 10-K body, an article, a long tool result' and query examples like 'supply-chain risk' and 'drug interactions with warfarin.' These examples clarify the intended usage better than the schema descriptions alone.

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 pair: 'Semantic search INSIDE a fetched record.' It clearly distinguishes from siblings by explicitly pairing with ask_pipeworx_grounded and explaining that it operates on already-pulled text, not whole documents. The examples of record types (SEC 10-K, article, long tool result) further clarify scope.

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?

Provides explicit when-to-use guidance: 'Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter.' It also names a specific complementary tool (ask_pipeworx_grounded) and the recommended workflow ('fetch with the gateway, ground over the relevant passages'), giving clear alternatives and integration context.

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