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

Beyond the readOnly/openWorld/idempotent annotations, the description reveals important runtime behavior: it fetches data via routed tools, extracts answers only from tool results, and returns explicit refusal reasons when the data doesn't directly answer. It also discloses the extra LLM call cost, adding transparency without contradicting 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 somewhat long but well-structured, front-loading the key purpose and then detailing outputs, refusal modes, use cases, and trade-offs. The mention of '5,892 across 1538 sources' is minor extra detail but not detrimental.

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?

Even without an output schema, the description fully specifies the success output shape, the refusal field and possible values, when to use the tool, and the cost trade-off. This is sufficient for an agent to invoke and interpret the tool 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 provides 100% coverage with descriptions for all six parameters, including alias relationships. The description does not add significant semantic information beyond what is in the schema, so the baseline score 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 clearly states this is a hallucination-resistant answer mode for high-stakes reads, and explicitly contrasts it with ask_pipeworx. It explains the routing and extraction behavior, making the tool's purpose immediate and distinct from siblings.

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 explicitly says to use it when an answer will be quoted, cited, or acted on, and to prefer ask_pipeworx for casual lookups. It also notes the extra LLM call cost, giving concrete selection criteria.

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