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

The description goes well beyond the annotations by detailing return shapes, evidence extraction as a verbatim quote, confidence, source, fetched_at, and explicit refusal reasons. It also discloses the extra LLM call cost. No contradiction with the readOnlyHint, openWorldHint, or idempotentHint 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 but well-organized: it leads with the core value proposition, explains the mechanism, gives return/refusal formats, provides usage guidance, and closes with a cost-based alternative. Each sentence carries useful information, and the length is justified by the complexity of the tool.

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 grounded answering tool with no output schema, the description covers the essential contract: what it does, how it works, what it returns, when it refuses, and when to use a cheaper alternative. It is sufficiently complete for an agent to select and invoke it 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?

The schema already documents the question parameter with 100% coverage, including natural-language input and aliases. The description does not add parameter-specific meaning beyond usage context, 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 defines the tool as a hallucination-resistant answer mode that routes to the appropriate source, fetches data, and extracts the answer strictly from the tool result. It names the exact behavior (grounded answering) and implicitly differentiates itself from ask_pipeworx by mentioning the same routing but a stricter extraction mechanism.

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 states when to use the tool: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' and gives concrete examples of high-stakes domains. It also provides a clear alternative and when to prefer it: '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.