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

Beyond the readOnly/idempotent annotations, the description discloses return shape, refusal reasons, evidence extraction, and the extra LLM call cost. It also states the limitation that the tool may refuse when data doesn't directly answer, which is valuable behavioral context that annotations alone do not provide.

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 contributes: definition, routing strategy, success response, refusal response, use cases, and cost/alternative. It is front-loaded with the core value proposition and does not waste words.

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?

For a tool with no output schema, the description adequately covers success and failure response shapes, refusal reasons, use contexts, and the comparison with the sibling tool. An agent has enough information to select and invoke it 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%, so the schema already documents the 'question' parameter and its aliases. The description adds no parameter-level meaning, but with full schema coverage, the 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 starts with a specific, differentiating behavior: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly identifies the resource (answer extraction from tool results) and distinguishes this from ask_pipeworx by emphasizing groundedness, evidence, and refusal behavior.

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: 'Use whenever an answer will be quoted, cited, or acted on.' It also names the alternative and the trade-off: 'prefer ask_pipeworx for casual lookups' and 'Costs 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.