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

Beyond the readOnly/openWorld/idempotent annotations, it discloses the exact success shape, the refusal contract with five enumerated refusal reasons, that it uses only tool-result content, and that it costs one extra LLM call. This gives the agent a precise model of the tool's behavior.

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 its purpose and then each sentence covers one necessary aspect: routing, return contract, usage, and cost tradeoff. Every sentence earns its place with no filler.

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?

With no output schema, the description fully specifies the return contract on both success and refusal paths, states cost and usage boundaries, and the schema covers all input parameters. An agent has everything it needs to invoke and interpret this tool correctly.

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?

The description does not add parameter-level meaning, but schema description coverage is 100% and the question parameter is already documented as natural language with aliases. No compensation is needed, so a baseline 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 it is a 'hallucination-resistant answer mode for high-stakes reads' and explains the grounded extraction behavior, distinguishing it from the sibling ask_pipeworx. It clearly identifies the operation (answer a question using only tool results) and names the sibling it diverges from.

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 explicitly says to use it when answers 'will be quoted, cited, or acted on' and when the agent 'must not invent facts', and to prefer ask_pipeworx for casual lookups. This is direct when-to-use and when-not-to-use guidance with a named alternative.

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