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

Annotations already declare readOnly/openWorld/idempotent, and the description enriches this by detailing the exact success return shape, explicit refusal reasons, the verbatim-evidence policy, and the extra LLM call cost. It clearly explains what happens when data doesn't directly answer, which is critical behavioral context beyond any annotation.

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: mode, mechanism, output contract, refusal contract, eligible use cases, and cost trade-off. It is front-loaded with the defining trait and uses compact structured notation for return values.

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 read-only grounded-answer tool with no output schema, the description is remarkably complete: it specifies the answer object, evidence behavior, confidence/source/fetched_at fields, refusal reasons, and comparison to the sibling tool. Nothing necessary for correct invocation or interpretation 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?

The schema already covers 100% of parameters, including a clear description of 'question' and all six aliases. The tool description adds no additional parameter-level meaning, so the schema-heavy 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 opens with a specific, distinctive purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly differentiates itself from ask_pipeworx by describing its grounded-extraction behavior and evidence-citing output, so an agent can tell them apart without inspecting schemas.

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?

Usage guidance is explicit and actionable: use it 'whenever an answer will be quoted, cited, or acted on' and facts must not be invented, with examples like financial verdicts and legal claims. It also names the alternative and the condition for preferring it: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.'

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