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

Annotations already signal readOnly and non-destructive behavior, but the description adds substantial behavioral detail beyond that: it extracts answers only from tool results, returns verbatim evidence, and produces explicit refusals with a fixed set of reasons. It also discloses the extra LLM call cost. No contradiction with annotations exists.

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 mostly earns its length: it front-loads the core purpose, then covers return shape, refusal reasons, usage context, and cost trade-off. A few specifics, like '5,743 across 1500 sources,' are vivid but not strictly necessary; still, there is no wasted padding.

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?

Even without an output schema, the description fully explains the return contract: success returns answer, evidence, confidence, source, fetched_at, and a null refusal_reason; failure returns a specific refusal_reason from an enumerated list. It also addresses cost and when to prefer the sibling, making the tool safe to invoke in high-stakes scenarios.

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%: all six parameters are documented as aliases of 'question.' The description does not add parameter-specific meaning beyond routing behavior, so the baseline 3 applies. The schema already carries the parameter semantics adequately.

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 specific mode—'Hallucination-resistant answer mode for high-stakes reads'—and explicitly contrasts it with ask_pipeworx: 'Same routing as ask_pipeworx... then EXTRACTS the answer using ONLY what the tool result contains.' This clearly identifies what the tool does and how it differs from its most similar sibling.

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?

The description gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also gives an exclusion by naming the cheaper alternative: 'prefer ask_pipeworx for casual lookups.' This is textbook usage differentiation.

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