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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description goes beyond by detailing the successful return shape ({answer, evidence, confidence, source, fetched_at, refusal_reason}) and the explicit refusal mechanism with specific refusal_reason values. This adds valuable context about failure modes and output structure that annotations do not provide. No contradiction with 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 longer than average but every sentence serves a purpose: it opens with the core value proposition, explains the mechanism, then gives usage guidance and cost trade-off. Well-structured and front-loaded with the most important information, no redundant 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?

Despite lacking an output schema, the description fully specifies the return object and its fields, including the refusal reasons. It covers the cost implication, the routing behavior, and the safety-critical use cases. For its complexity (variant tool with distinct behavior), the description is complete enough for an agent to call it correctly without ambiguity.

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 description coverage is 100% — all parameters are aliases for 'question' and explicitly documented. The description does not add parameter-level detail beyond what the schema already provides. The baseline of 3 is appropriate because the schema fully describes the parameters.

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 a specific verb (ask), resource (Pipeworx), and mode (grounded). It distinguishes itself from its sibling ask_pipeworx by adding an extraction step that uses only tool results, preventing hallucination. A clear use case is stated.

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 (high-stakes reads where answers will be quoted, cited, or acted on, and when factual integrity is critical) and when not to (casual lookups, prefer ask_pipeworx). The trade-off of an extra LLM call is transparently explained, giving the agent clear decision criteria.

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