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

Annotations already mark readOnly/openWorld/idempotent, but the description adds substantial behavior beyond that: the refusal protocol with five enumerated refusal_reason values, the exact success return shape (answer, evidence as verbatim quote, confidence, source, fetched_at), the guarantee of extraction 'using ONLY what the tool result contains', and the extra LLM call cost. No contradiction with the annotations — the readOnlyHint aligns with the fetch-and-read nature.

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 dense but efficiently structured: purpose → mechanism → return contract → usage guidance → cost tradeoff. Every sentence earns its place and the primary purpose is front-loaded. It runs slightly long, but for a tool with refusal semantics and a return contract this complexity is largely justified.

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?

There is no output schema, so the description must document the return contract — and it does so thoroughly for both the success and refusal paths, including the exhaustive refusal_reason enum. Given the high complexity (routing across 5,798 tools, extraction constraint, refusal modes) the description is complete: an agent knows what it returns, when to call it, when not to, and what behavioral guarantees to expect.

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 single required 'question' parameter is fully documented with all six aliases (query, q, prompt, text, input). The description adds no parameter-level meaning beyond the schema, which is appropriate since the schema already carries the burden. Baseline 3 applies.

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, meaningful framing — 'Hallucination-resistant answer mode for high-stakes reads' — and states the exact mechanism (same routing as ask_pipeworx, then extracts the answer using ONLY what the tool result contains). It distinguishes itself from the sibling ask_pipeworx by naming it directly and contrasting the casual vs. high-stakes use case. An agent can tell this tool apart from its siblings immediately.

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 gives explicit when-to-use guidance ('Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') with concrete examples (financial verdicts, legal claims, medical lookups, public statements), and explicit when-not-to guidance ('prefer ask_pipeworx for casual lookups'), including the cost tradeoff of one extra LLM call. This is the ideal level of routing clarity.

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