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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description clearly discloses the grounded extraction behavior, the structured success output, and the explicit refusal contract with refusal reasons. It also discloses the extra LLM call cost. Nothing contradicts the 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 every sentence carries information: purpose, routing behavior, output contract, refusal reasons, usage criteria, and cost comparison. It is front-loaded with the most important differentiator and structured logically.

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 covers what the tool does, how it behaves, when to use it, what it returns, what refusal looks like, and how it compares to the sibling tool. For a high-stakes read tool with no output schema, this is complete enough for an agent to select and invoke it 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?

The schema covers 100% of parameters, so the baseline is 3. The description adds context about the tool filling arguments itself and the refusal behavior, but it does not add parameter-specific semantics beyond the schema definitions.

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 opens with a specific, differentiated purpose: 'Hallucination-resistant answer mode for high-stakes reads' and directly contrasts itself with ask_pipeworx by describing the same routing plus a grounded extraction step. An agent can clearly tell this tool apart from ask_pipeworx and ask_pipeworx_beta without inspecting schemas.

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?

Explicit when-to-use guidance is provided: use whenever answers will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). It also explicitly says 'prefer ask_pipeworx for casual lookups' and notes the extra LLM call cost, making the decision criterion actionable.

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