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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description discloses the extra LLM call cost, the routing mechanism, and the full refusal reason taxonomy (not_in_source, no_tool_match, tool_error, data_truncated, llm_error). No contradiction with annotations; it adds rich behavioral context that annotations cannot capture.

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 moderately long but every sentence carries value: purpose, mechanism, return format, usage guidance, and cost trade-off. It is front-loaded with the key selling point (hallucination-resistant) and structured logically. Slightly verbose but not wasteful.

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?

Given the absence of an output schema, the description fully explains the return structure and refusal cases. It covers the routing behavior, the data-fetching steps, and the explicit use-case boundaries. An agent has all necessary information to decide when to call it and interpret results.

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 documents all six parameters as aliases for the question string, with 100% coverage. The description adds no new semantic information about the parameter beyond what the schema states, so the baseline of 3 applies. It doesn't clarify which alias to prefer, but the schema already says they are interchangeable.

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 states a specific verb (ask/extract) and resource (Pipeworx data across 5,801 tools/1,517 sources), and clearly distinguishes itself from ask_pipeworx by the grounded extraction mechanism. It also defines the exact success and refusal return shapes, making its purpose unambiguous.

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 it ('whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts') and when not to ('prefer ask_pipeworx for casual lookups'), directly naming the alternative. This gives clear decision criteria for the agent.

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