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

The description goes well beyond the annotations by disclosing the internal routing process, the strict extraction-only-from-tool-result behavior, the full refusal contract with specific refusal_reason values, and the return shape with evidence, confidence, and fetched_at. It also discloses the extra LLM call cost. This is rich behavioral context that the 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 but efficient: it front-loads the core value proposition, explains routing, gives the exact return and refusal contracts, and closes with cost-based usage guidance. Every sentence earns its place, and the structured enumeration of refusal reasons is valuable rather than verbose.

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 high-stakes, no-output-schema tool with 6 parameters, the description is complete: it specifies what the tool returns, how it refuses, when to use it, when not to use it, and how it compares to ask_pipeworx. An agent has everything needed to select and invoke this 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?

Schema description coverage is 100%, and every parameter is an alias for the same natural-language question. The description confirms the question-based nature of the tool but does not need to add parameter-level detail because the schema already fully documents all aliases. Baseline 3 is appropriate that the schema carries the parameter burden.

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 behavior: a hallucination-resistant, grounded answer mode for high-stakes reads. It names the resource (Pipeworx sources) and explicitly contrasts itself with ask_pipeworx by emphasizing extraction only from tool results. This is a sharp, specific purpose, not a restatement of the title.

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?

Usage guidance is explicit and actionable: use when answers will be quoted, cited, or acted on and facts must not be invented; prefer ask_pipeworx for casual lookups due to the extra LLM call cost. It even names the alternative tool and the deciding condition, leaving no room for inference.

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