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

Discloses substantial behavior beyond the annotations: refusal modes, success/refusal return shapes, the constraint that the answer uses only the tool result, and the extra LLM call cost. Annotations already mark it read-only, idempotent, and non-destructive, so the description adds meaningful operational context without contradicting them.

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?

Dense but purposeful: front-loads the core value, then the mechanism, return contract, usage, and tradeoff. Every sentence earns its place; no filler or repetition of schema or annotation content.

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?

Complete for a complex read-only query tool: it explains what the tool does, how it behaves, what it returns on both success and refusal, when to prefer it, and when to avoid it. With no output schema, the description fully carries the burden of return-value documentation.

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% and the schema already documents the question parameter and aliases clearly. The description does not add further parameter-level meaning, 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?

States a specific purpose: a hallucination-resistant answer mode that extracts answers only from tool results. Explicitly differentiates itself from ask_pipeworx by naming it and explaining the shared routing plus the added extraction step.

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?

Provides explicit when-to-use guidance: for high-stakes, quotable, cited, or acted-on answers where facts must not be invented. Also gives the alternative directly: prefer ask_pipeworx for casual lookups, including 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.