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

Annotations already declare readOnly, idempotent, and non-destructive hints. The description adds meaningful behavioral details beyond those: it explains the grounded extraction process, the explicit refusal mechanism with enumerated refusal reasons, and the extra LLM call cost. These are important operational behaviors not covered by 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 well-structured with clear sentences, but some redundancy exists (e.g., 'then EXTRACTS' and 'when the data doesn't directly answer' are repeated in slightly different forms). It is not overly long, but trimming repetition would make it tighter.

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 tool has no output schema, the description compensates by specifying the return shape (answer, evidence, confidence, source, fetched_at, refusal_reason) and the refusal reasons. It also provides cost context and comparison with the sibling tool, making it self-sufficient for an agent to decide when and how to invoke it.

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% with all six parameters described as aliases for 'question'. The description adds that the parameter is a natural language question and confirms the alias list, which is slightly redundant but helpful. It does not fully explain how the question is processed or what constraints exist (e.g., length limits), so a 4 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 the tool as a hallucination-resistant answer mode for high-stakes reads, contrasting it with the sibling ask_pipeworx for casual lookups. It explicitly names the resource (Pipeworx) and the primary action (grounded answering), making the 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?

The description explicitly states when to use the tool ('whenever an answer will be quoted, cited, or acted on') and when to prefer the alternative ('prefer ask_pipeworx for casual lookups'). This provides clear, actionable guidance on tool selection.

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