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

The description substantially enriches the annotations by specifying the exact success return shape (answer, evidence, confidence, source, fetched_at, refusal_reason) and the refusal contract with enumerated refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error). It also discloses the extra LLM call cost and the grounding constraint ('using ONLY what the tool result contains'). This goes well beyond the readOnly/openWorld/idempotent hints.

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: purpose, mechanism, return structure, refusal contract, use cases, and cost tradeoff. It is front-loaded with the core distinguishing trait (hallucination-resistant) and progressively adds operational detail. No fluff or repetition.

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?

There is no output schema, so the description carries full responsibility for return-value documentation. It fully specifies both success and failure response shapes, including the refusal reason enum, and explains the behavioral implications of grounding. Given the tool's complexity (routing across thousands of tools), the coverage is complete.

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% with 'question' fully documented and aliases enumerated, so the baseline is 3. The description adds context that the input question is used to pick a tool and fill arguments, but this is not parameter-specific enough to exceed the baseline. The schema already describes the natural-language semantics.

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 clearly defines the tool as a 'hallucination-resistant answer mode' that routes questions to the right underlying tool, fetches data, and extracts answers grounded solely in the tool result. It explicitly distinguishes itself from ask_pipeworx by highlighting the grounded extraction and refusal behavior, making the purpose and differentiation unmistakable.

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' with concrete high-stakes examples (financial verdicts, legal claims, medical lookups, public statements). It also names the alternative and the tradeoff: 'Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.' This leaves no ambiguity about selection.

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