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

Goes far beyond the readOnly/openWorld/idempotent annotations by disclosing the exact success return shape ({answer, evidence, confidence, source, fetched_at, refusal_reason:null}), the full refusal_reason enum with five values, the verbatim-quote evidence contract, and the constraint that extraction uses only tool-result content. Nothing contradicts the 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?

Dense but zero-waste: core identity first, then mechanism, then the return/refusal contract, then selection guidance and cost. Every sentence earns its place, and since there is no output schema, the detailed return-shape disclosure is necessary rather than padding.

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?

Complete for a read-only, schema-documented tool: an agent can select it, invoke it, and interpret both success and refusal outputs without guessing. The cross-reference to ask_pipeworx is legitimate given the sibling list, and annotations cover the safety profile.

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 description coverage is 100% — the question parameter and all five aliases are documented with type, format, and natural-language guidance in the schema itself. The description adds no parameter-level detail, so the baseline 3 applies; the schema correctly carries the parameter burden.

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?

Opens with a specific, differentiating identity — 'Hallucination-resistant answer mode for high-stakes reads' — followed by a concrete mechanism: routes across 5,743 tools, fills arguments, fetches data, then extracts the answer using ONLY the tool result. It frames itself against the sibling ask_pipeworx rather than restating the name.

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 names the alternative (ask_pipeworx), the selecting condition ('whenever an answer will be quoted, cited, or acted on'), concrete high-stakes examples (financial, legal, medical, public statements), and the exclusion ('prefer ask_pipeworx for casual lookups') with the cost tradeoff of one extra LLM call. An agent needs zero inference to choose correctly.

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