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

Beyond the annotations, the description discloses important behavioral traits: it only uses tool result content, returns a refusal object with specific refusal_reason values when it can't answer, and costs one extra LLM call. This gives the agent a clear model of failure modes and cost without contradicting the 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 front-loaded with the core purpose, then provides the return shape, refusal reasons, use cases, and cost trade-off in a compact sequence. Every sentence contributes actionable information, and the structure makes the key distinctions easy to scan.

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 having no output schema, the description fully documents the success and refusal response structures, including refusal_reason values. It covers use cases, exclusions, cost, and behavioral guarantees, so 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 coverage is 100% and the schema already documents question and all six aliases clearly, so the description doesn't need to add parameter syntax. The description adds context about how the question is used but no parameter-level detail beyond what the schema provides. 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?

The description clearly identifies a specific, distinct mode: a hallucination-resistant answer mode that extracts answers only from the tool result and refuses when the data doesn't directly answer. It differentiates itself from ask_pipeworx by name and by emphasizing evidence and refusal behavior, so an agent can distinguish it immediately.

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 gives explicit when-to-use guidance: high-stakes reads where answers will be quoted, cited, or acted on, such as financial verdicts, legal claims, medical lookups, and public statements. It also explicitly names the alternative (ask_pipeworx) and says to prefer it for casual lookups, plus notes the extra LLM call cost.

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