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

Even though annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, the description adds a full behavioral contract: success returns a structured result with answer, verbatim evidence, confidence, source, and fetched_at; failure returns an explicit refusal with a discrete set of refusal_reason values. It also discloses the extra LLM call cost. No contradiction with 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?

The description is dense but every sentence earns its place: purpose, routing, mechanism, return contract, refusal behavior, use cases, and cost trade-off versus the sibling. It is front-loaded with the most important discriminator ('hallucination-resistant') and ends with practical cost guidance. No filler or redundancy.

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 fully compensates by enumerating the success and refusal return shapes and all refusal reason values. It also covers sibling differentiation, cost, and usage context. For a tool with a single question parameter and rich behavioral nuance, nothing essential is missing.

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 single required parameter is self-explanatory ('Your question in natural language') with alias support documented. The description adds no new parameter-level semantics, but the schema fully covers parameter meaning. A baseline of 3 is appropriate.

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 states a precise purpose: a hallucination-resistant answer mode for high-stakes reads. It clearly differentiates itself from ask_pipeworx by noting it uses the same routing but then extracts answers only from the tool result and can refuse to answer. This gives an agent a clear, distinct mental model of the tool's function.

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?

It gives explicit when-to-use guidance: whenever the answer will be quoted, cited, or acted on, and when the agent must not invent facts. It also gives an explicit alternative and when-not-to-use: prefer ask_pipeworx for casual lookups, because this costs one extra LLM call. This is unambiguous routing advice.

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