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

The description adds substantial behavioral detail beyond the annotations: it performs routing across 5,738 tools, fills arguments, fetches data, extracts answers only from tool results, costs an extra LLM call, and returns explicit structured refusals with specific refusal_reason values. These details are not present in the annotations and are highly relevant to an agent invoking the tool.

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 well-organized: it starts with the core value proposition, then explains behavior and output shape, then gives usage guidance and cost trade-offs. It is longer than a typical description, but most sentences carry unique information; the only mild redundancy is restating the broad routing behavior in the opening clause.

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 description is complete for practical use: it explains what the tool does, how it behaves differently from ask_pipeworx, when to use it, what the output looks like including refusal cases, and what the cost trade-off is. Given the rich annotations and fully covered input schema, nothing essential is missing for an agent to choose 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?

All six parameters are documented aliases for the same natural-language question field, and schema description coverage is 100%, so the schema already fully explains the parameters. The description adds contextual value by explaining how the question is used internally (routing and data fetching), but it does not need to add parameter-level detail because the schema covers it completely.

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 this as a hallucination-resistant grounded answer mode that routes a question to the right source tool, fetches data, and extracts an answer only from the retrieved result. It explicitly distinguishes itself from ask_pipeworx by emphasizing high-stakes reliability and refusal behavior, so an agent can tell it apart from its sibling tools.

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 guidance on when to use this tool ('whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'). It also names the alternative and explains the trade-off: one extra LLM call for grounding.

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