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

The description goes well beyond the annotations by disclosing the refusal behavior, explicit refusal_reason enum, evidence extraction with verbatim quotes, and the extra LLM call cost. It clearly explains that the tool will refuse rather than fabricate when data does not directly answer, which is critical behavioral context for an agent. The description is consistent with the annotations, which already indicate read-only, idempotent, non-destructive behavior.

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 information-dense but well-structured: it opens with the core purpose, then explains the mechanism, output/refusal shape, ideal use cases, and tradeoff versus the sibling tool. Every sentence adds operational value, and the most important differentiator — hallucination resistance — is front-loaded.

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 tool with a single required parameter, high schema coverage, and rich annotations, the description covers everything an agent needs to invoke it correctly: when to use it, what it returns, how it refuses, and why it differs from the casual alternative. The explicit refusal_reason set and the cost tradeoff make the tool's behavior predictable in edge cases, and no output schema is needed because the return shape is fully described.

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 input schema already covers 100% of parameter semantics, including the primary 'question' parameter and all five aliases, so the description does not need to add parameter-level detail. The description focuses on tool behavior and use cases rather than parameter meanings, which is acceptable given the high schema coverage. Per the baseline for high schema coverage, a 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 clearly identifies this as a hallucination-resistant, grounded answer mode for high-stakes reads, with a specific verb, resource, and behavior. It explicitly distinguishes itself from the sibling ask_pipeworx by noting the same routing but stricter evidence-based extraction, so an agent can differentiate them without opening the schema.

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 states exactly when to use this tool: whenever an answer will be quoted, cited, or acted on and the agent must not invent facts, giving concrete examples like financial verdicts and legal claims. It also explicitly says to prefer ask_pipeworx for casual lookups because this mode costs an extra LLM call, providing clear when-to-use versus when-not-to-use guidance.

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