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

The description goes well beyond annotations by disclosing the internal routing across 5,724 tools/1497 sources, the extraction-only constraint, the structured success envelope ({answer, evidence, confidence, source, fetched_at, refusal_reason:null}), and explicit refusal reasons. It also explains the extra LLM call cost, which aligns with the openWorldHint and readOnlyHint annotations. No contradiction.

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 purposeful, front-loading the key distinguishing trait (hallucination-resistant) and then providing routing behavior, return shape, refusal reasons, and usage guidance. It is longer than ideal but every sentence carries information an agent needs to choose and invoke the tool correctly.

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, idempotent tool with a simple single-required-parameter schema and no output schema, the description fully covers selection criteria, behavioral expectations, return shape, refusal cases, and cost tradeoffs. The only thing absent is a concrete example, but the natural-language question parameter makes that unnecessary.

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% because the schema describes the question parameter and lists five aliases. The description adds natural-language usage guidance and the behavioral impact of the question, so it adds some semantic context beyond the schema. Baseline 3 would apply; the description's mention of 'fills arguments' and the answer/refusal envelope provides a bit more, so 4.

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 ('answer mode') with a clear resource (Pipeworx) and a distinctive behavioral trait: hallucination resistance via extraction only from tool results. It distinguishes itself from the sibling ask_pipeworx by naming it and contrasting the grounded mode with casual lookups.

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?

Explicitly says when to use: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements).' It also tells when not to prefer it: 'prefer ask_pipeworx for casual lookups,' and explains 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.