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

Beyond the readOnly/idempotent annotations, the description discloses the exact success return shape, the explicit refusal mechanism, all refusal_reason enum values, the constraint that answers come only from tool results, and the cost of one extra LLM call. This is substantial behavioral context that annotations alone do not provide.

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 dense and well-organized: purpose, mechanics, return contract, usage guidance, and cost tradeoff. It is longer than minimal, but every sentence contributes; the detailed return schema and refusal reasons earn their place. Slightly longer than ideal but not bloated.

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?

The tool has no output schema, yet the description fully specifies the success and failure return shapes. It also explains routing behavior, refusal semantics, and the tradeoff with ask_pipeworx. An agent has everything needed to decide when to call it and what to expect.

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%, including all six aliases for the single required parameter. The description does not add parameter-level semantics, but the schema already fully documents the natural-language question and its aliases, 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?

The description names a specific mode ('hallucination-resistant answer mode'), the resource ('Pipeworx'), and the core behavior: route, fetch, and extract the answer only from tool results. It also explicitly contrasts with the sibling ask_pipeworx, so an agent can distinguish it 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?

It gives explicit when-to-use guidance: high-stakes reads, quoted/cited/acted-on answers, financial verdicts, legal claims, medical lookups, public statements. It also states when NOT to use it: casual lookups should prefer ask_pipeworx because of the extra LLM call.

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