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

Annotations already mark readOnly/openWorld/idempotent, but the description adds substantial behavioral detail: it extracts using ONLY tool result contents, can refuse with specific refusal_reason codes, includes verbatim evidence, and costs one extra LLM call. This goes well beyond the structured annotations and presents no contradiction.

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, front-loading the core differentiator ('Hallucination-resistant answer mode') and then covering behavior, output shape, refusal reasons, use cases, and tradeoff in order. Each sentence earns its place without unnecessary padding.

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 there is no output schema, the description compensates by fully specifying the success and refusal response shapes. It also provides source context, cost implications, and clear usage criteria, making it sufficiently complete for an agent to invoke 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?

Schema description coverage is 100% and the schema already documents the question parameter and all aliases. The description adds no new parameter-level semantics, so a baseline score of 3 is appropriate since the structured schema carries the burden.

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 identifies a specific verb-resource pair ('ask Pipeworx — Grounded') and clearly distinguishes it from ask_pipeworx by emphasizing hallucination-resistant, evidence-grounded extraction. It names its key behavior — returns {answer, evidence, confidence, source, fetched_at} or an explicit refusal — which separates it from 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?

It explicitly states when to use this tool: 'whenever an answer will be quoted, cited, or acted on' and when to avoid it: 'prefer ask_pipeworx for casual lookups.' It also references the alternative (ask_pipeworx) and gives concrete high-stakes contexts, leaving little to inference.

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