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

Annotations already declare readOnly, idempotent, and non-destructive behavior, and the description adds substantial context beyond that: refusal reasons, the constraint of using only tool result content, the explicit refusal shape, and the extra LLM call cost. No annotation contradiction exists.

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 every sentence earns its place: it front-loads the core mode, explains routing and extraction behavior, gives the full return/refusal shape, provides use cases, and states the cost tradeoff. The length is justified by the absence of an output schema and the tool's high-stakes context.

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 complex tool with no output schema, the description is remarkably complete: it specifies the success return fields, all refusal reasons, the extraction constraint, when to use it, when not to, and the cost difference versus the sibling. An agent has everything needed to invoke it correctly and interpret its result.

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 all six parameters clearly documented as aliases for the natural-language question. The description adds no parameter-level detail beyond what the schema already provides, so the baseline 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 verb and resource: a grounded, hallucination-resistant answer mode that extracts answers only from fetched tool results. It distinguishes itself from ask_pipeworx by name, clarifies the shared routing, and gives a concrete return shape. An agent can tell exactly what this tool does and how it differs from its sibling.

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 facts must not be invented, with examples of high-stakes domains. It also names the alternative (ask_pipeworx) and states when to prefer it, 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.