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

Annotations already mark readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, and the description adds valuable behavioral context: it uses ONLY the tool result, returns explicit refusal reasons on insufficient data, and costs an extra LLM call. No contradiction exists between description and 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 and front-loaded: purpose first, then mechanism, then return contract and usage guidance. Every sentence—including the refusal-reason enum and cost tradeoff—earns its place for a high-stakes tool with no output schema.

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 compensates by fully specifying the success return shape (answer, evidence, confidence, source) and the refusal behavior with enumerated reasons. Combined with complete parameter schema and safety annotations, an agent has everything needed to select and invoke 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 coverage is 100%, with the question parameter and aliases fully documented in the input schema. The description does not add parameter-level meaning beyond the schema, so 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 states a specific verb and resource—'hallucination-resistant answer mode for high-stakes reads'—and clearly positions it relative to ask_pipeworx by describing the same routing but with extraction restricted to tool results. It distinguishes this tool from its sibling by emphasizing grounded, evidence-backed answers with refusal semantics.

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 when to use this tool: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' with concrete domains like financial verdicts and legal claims. It also gives the when-not-to-use case by advising to 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.