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

Beyond the annotations, the description discloses the full refusal contract, including exact refusal_reason values, evidence fields, and the guarantee that answers come only from tool results. This gives the agent a clear model of success and failure behavior without contradicting the read-only, idempotent 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 well-structured: purpose, mechanism, return contract, refusal contract, usage guidance, and cost tradeoff. It is front-loaded with key information and every sentence contributes meaningful behavioral or routing context.

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 both success and refusal return shapes, making the tool's response behavior predictable. Combined with the schema-covered input and annotations covering safety, an agent has everything needed to select, 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?

Schema description coverage is 100% and the schema already documents the question parameter and its aliases. The description adds no parameter-specific detail, but the baseline of 3 is appropriate because the schema carries the parameter documentation burden and does so completely.

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 purpose: hallucination-resistant answer mode for high-stakes reads. It clearly distinguishes this tool from ask_pipeworx by stating it extracts answers using only tool-result content and returns explicit refusals when data doesn't answer.

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?

Explicit when-to-use guidance is present: use when answers will be quoted, cited, or acted on, and when the agent must not invent facts. It also gives the exclusion: prefer ask_pipeworx for casual lookups, and notes the cost tradeoff of one extra LLM call.

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