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

Beyond the annotations (read-only, idempotent, open-world, non-destructive), the description reveals critical behavioral traits: it extracts answers solely from tool results, returns a structured evidence object with verbatim quotes, and explicitly refuses with categorized refusal_reason values when data is insufficient. This significantly exceeds what annotations alone convey.

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 purpose and using structured lists for return shapes and refusal reasons. The inclusion of precise source/tool counts and detailed response contract is justified for a high-stakes tool, though slightly longer than strictly needed.

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?

The description completely covers what the tool does, when to use it, how it differs from ask_pipeworx, what success and refusal responses look like, and the cost/performance trade-off. With no output schema, the explicit return format and refusal reasons are essential and provided thoroughly.

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%, with the question parameter fully described including aliases. The tool description adds no additional parameter-level meaning—it only refers to 'fills arguments' generically—so the schema carries the semantics and baseline 3 applies.

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 grounded, hallucination-resistant answer mode for high-stakes reads, explicitly contrasting it with ask_pipeworx. It specifies the resource ('Pipeworx') and the behavioral verb ('answers using only tool result contents'), making the purpose unmistakable and distinct from siblings like validate_claim or deep_research.

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 usage guidance: use when an answer will be quoted, cited, or acted on, especially for financial, legal, or medical facts, and prefer ask_pipeworx for casual lookups. This directly addresses when to choose this tool over its sibling and even states the cost trade-off (extra LLM call), leaving no ambiguity.

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