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

Discloses detailed behavior beyond annotations: the tool returns a structured success object with evidence as a verbatim quote and enumerated refusal reasons when data does not directly answer. Thes adds genuinely useful operational context and does not conflict with the readOnly, openWorld, idempotent, non-destructive 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?

Every sentence carries distinct payload: purpose, routing mechanics, return contract, use cases, and cost trade-off. The structure front-loads the primary purpose and then layers behavior and usage guidance, making it easy for an agent to quickly grasp what this tool is for.

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?

Despite lacking an output schema, the description fully specifies the success and refusal return shapes, including exact field names and refusal reason values. It also contextualies the tool against ask_pipeworx including cost implications and provides concrete high-stakes usage scenarios, making it complete for safe invocation.

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%, so aliases like query, q, prompt, text, and input are already documented. The description itself does not add any parameter-specific meaning, but it does not need to because the schema fully explains the question parameter.

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 this as a hallucination-resistant answer mode that routes to the appropriate tool and extracts answers only from tool results. It explicitly distinguishes itself from its sibling ask_pipeworx by emphasizing grounded, evidence-backed answers rather than open-ended responses.

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: whenever an answer will be quoted, cited, or acted on and the agent must not invent facts, with concrete domains like financial verdicts, legal claims, medical lookups, public statements. It also gives a direct preference rule: prefer ask_pipeworx for casual lookups because this mode costs an extra LLM call.

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