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

Discloses substantial behavior beyond the annotations: the extra LLM call cost, the refusal contract with enumerated refusal_reason values, and the exact return shape including evidence as a verbatim quote, confidence, source, and fetched_at. The annotations (readOnly, openWorld, idempotent) cover safety but the description adds the operational behavior an agent needs. No contradiction — the openWorldHint is consistent with routing across 5,743 tools while the extraction remains grounded in the returned data.

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?

Front-loaded with the key differentiator and densely packed with the return contract, refusal reasons, selection criteria, and cost tradeoff. Slightly longer than necessary, but the length is justified because there is no output schema, so the explicit return/refusal format earns its place.

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 complex tool: routing behavior, success return shape, refusal semantics with all possible reasons, selection context, and cost implications are all specified. Despite the absence of an output schema, an agent has everything needed to call it correctly and interpret the result, including the refusal contract that explains what happens when the data does not directly answer.

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% and the schema already documents the single required parameter 'question' along with five aliases (query, q, prompt, text, input). The description adds no parameter-level detail, but with full schema coverage the baseline of 3 applies — the schema carries the burden and does so adequately.

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?

States a specific mode ('hallucination-resistant answer mode for high-stakes reads') with a clear verb and resource, and differentiates itself from the sibling ask_pipeworx by describing its distinguishing behavior: it extracts the answer using ONLY what the tool result contains. An agent can tell this apart from ask_pipeworx, ask_pipeworx_beta, and deep_research without inspecting further.

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?

Gives explicit when-to-use guidance with concrete high-stakes scenarios ('financial verdicts, legal claims, medical lookups, public statements') and an explicit when-not-to-use via the cost tradeoff ('prefer ask_pipeworx for casual lookups'). The alternative tool is named directly, leaving nothing to inference.

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