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

Beyond the readOnly/openWorld/idempotent annotations, the description reveals critical behavior: it routes across thousands of sources, extracts only from tool results, returns an explicit refusal with specific refusal_reason values, and costs one extra LLM call. This is rich, actionable behavioral disclosure.

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 but every sentence earns its place: purpose, routing behavior, return contract, refusal reasons, use cases, and cost tradeoff. The key differentiator is front-loaded in the first sentence.

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?

With no output schema, the description fully specifies both the success return shape and the refusal return shape, including refusal_reason enum values. An agent has enough context to invoke the tool and interpret the result without additional documentation.

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 coverage is 100%, and all parameters are effectively aliases for the natural-language question. The description does not add parameter-level guidance beyond the schema, but the schema fully documents the accepted aliases. This meets the baseline for high schema coverage.

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 opens with 'Hallucination-resistant answer mode for high-stakes reads,' which is a specific verb-plus-scope statement. It then distinguishes itself from ask_pipeworx by describing the grounded extraction behavior and the exact success/refusal contract.

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?

Explicitly states when to use: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names the alternative and the condition for choosing it: 'prefer ask_pipeworx for casual lookups.'

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