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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses that the tool refuses to answer when data doesn't directly support an answer, lists exact refusal reasons, and notes the extra LLM call cost. This adds meaningful behavioral context that annotations do not 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 information-dense and efficiently structured: purpose first, then return format, refusal behavior, usage conditions, and cost tradeoff. Every sentence contributes operational value with no redundancy or filler.

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?

With no output schema, the description fully specifies the return shape including success fields and refusal reasons. It also covers usage context, alternatives, and cost implications, making it complete for an agent to decide when and how to invoke the tool.

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 the schema already documents that question accepts aliases (q, query, prompt, text, input). The description does not add parameter-level semantics beyond reiterating that the tool fills arguments and fetches data. Since the schema fully covers the parameter meaning, a baseline of 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 the tool as a hallucination-resistant answer mode for high-stakes reads, with a specific verb (ask) and resource (Pipeworx). It explicitly contrasts with ask_pipeworx by stating it EXTRACTS the answer using ONLY what the tool result contains, making the distinction between the two clear.

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 provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' and gives concrete examples of high-stakes domains. It also states when not to use it: 'prefer ask_pipeworx for casual lookups,' and explains the cost tradeoff of one extra LLM call.

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