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

Beyond the readOnly/idempotent/openWorld annotations, the description discloses the full behavioral contract: grounded extraction from tool results only, the exact success shape {answer, evidence, confidence, source, fetched_at, refusal_reason:null}, and explicit refusal with a complete refusal_reason enum. It also reveals the cost penalty (one extra LLM call), which annotations cannot convey.

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?

Information-dense but front-loaded: the differentiator, mechanism, return shapes, usage rule, and cost tradeoff each earn their place. Minor deduction for the volatile specificity '5,798 across 1517 sources,' which risks staleness and is not essential to correct invocation.

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?

Although the tool is complex (routing across thousands of sub-tools) and has no output schema, the description fully specifies both success and refusal return shapes, the verbatim-evidence guarantee, and cost/usage tradeoffs. The simple 1-parameter schema and safety annotations cover the rest. Nothing needed for correct invocation 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?

Schema description coverage is 100% — the single required 'question' parameter and its six aliases are fully documented in the schema itself, so the description needs to add no parameter-level meaning. The description's mention of internal argument-filling is behavioral context, not parameter semantics. Baseline 3 applies.

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, differentiating claim — 'Hallucination-resistant answer mode for high-stakes reads' — and explicitly contrasts with the sibling ask_pipeworx ('Same routing as ask_pipeworx... then EXTRACTS the answer using ONLY what the tool result contains'). An agent can immediately tell this apart from ask_pipeworx and ask_pipeworx_beta in the sibling list without opening their 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 whenever an answer will be quoted, cited, or acted on' with concrete domains (financial verdicts, legal claims, medical lookups, public statements), and the tradeoff is stated — 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This gives both when-to-use and when-not-to-use with the alternative named.

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