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

Beyond the annotations (readOnly, idempotent), the description discloses the tool's refusal behavior, the exact success and refusal response shapes, refusal reason enum values, verbatim evidence quoting, and the extra LLM call cost. This is far richer than the structured annotations alone and gives the agent realistic expectations about grounding and failure modes.

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?

Every sentence earns its place: the opening states the core value, the middle explains the mechanism and output contract, and the closing covers use cases and cost trade-offs. It is dense but not bloated, and the most important information appears first.

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 the simple single-question input and no output schema, the description fully compensates by specifying the complete return shape for success and refusal, the refusal reasons, evidence requirements, and when to avoid the tool. An agent has everything it needs to invoke the tool and interpret its response 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%, so the parameter semantics are already fully documented in the schema, including aliases and natural language guidance. The description adds no additional parameter-level detail beyond describing the tool's internal argument-filling behavior, which is not necessary for the caller to compose the question correctly.

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 defines a specific mode—'Hallucination-resistant answer mode for high-stakes reads'—and clearly differentiates it from the sibling ask_pipeworx by emphasizing extraction using only the tool result, with explicit refusals instead of invented facts. This gives an agent an unambiguous understanding of what the tool does and how it differs from related 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?

The description states exactly when to use the tool: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also gives the alternative: 'prefer ask_pipeworx for casual lookups,' and notes the extra LLM call cost. This is explicit, actionable guidance.

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