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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description goes well beyond this by revealing the refusal mechanism, enumerating possible refusal_reason values, and disclosing the extra LLM call cost. It also states that answers are grounded strictly in the tool result, adding important behavioral context not available in 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 dense but highly organized: it front-loads the core value proposition, then covers mechanism, return contract, refusal reasons, use cases, and cost comparison. Every sentence adds distinct information needed for correct selection and invocation, 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?

There is no output schema, so the description carries the full burden of explaining return values. It explicitly specifies the success object ({answer, evidence, confidence, source, fetched_at, refusal_reason}) and the refusal object ({answer:null, refusal_reason}). It also covers routing, cost implications, and when to prefer the alternative, making the tool fully understandable in context.

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 fully documents the single meaningful parameter ('question') with all aliases and has 100% schema_description_coverage. The description does not add parameter-specific semantics beyond restating that the input is a natural-language question, so the 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 states a specific verb and resource ('Ask Pipeworx') and clearly defines the mode as 'hallucination-resistant answer mode'. It explicitly contrasts itself with the sibling tool by saying 'Same routing as ask_pipeworx' while noting it 'EXTRACTS the answer using ONLY what the tool result contains'. This makes its purpose and differentiation immediately 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?

Provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' with concrete examples. It also gives an explicit alternative condition: 'prefer ask_pipeworx for casual lookups', directly naming the sibling and the cost trade-off.

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