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

Beyond the readOnlyHint and idempotent annotations, the description discloses the full success/failure contract: returns {answer, evidence, confidence, source, fetched_at, refusal_reason:null} or an explicit refusal with enumerated reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error). It also states the extraction is constrained to the tool result, which is critical behavioral context.

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 information-dense and front-loaded with the purpose, then details the routing, return shape, refusal reasons, and usage. It is slightly long but every sentence earns its place, and the structure is logical. Not bloated.

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?

It covers the routing mechanism, success/refusal return shapes, refusal reason taxonomy, cost trade-off, and high-stakes use cases. No output schema exists, so the description fully compensates by specifying return structure. No critical information is missing for an agent to 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 already fully documents all six parameters as aliases for the question, with a description for each and 100% coverage. The description adds no parameter-specific guidance beyond the conceptual routing behavior, so it contributes little beyond the schema. Baseline 3 is appropriate.

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 states it is a 'Hallucination-resistant answer mode for high-stakes reads' and explicitly differentiates from ask_pipeworx by the extraction behavior (uses ONLY tool result) and the additional LLM call cost. It names the exact use cases (quoted, cited, acted-on answers) and the resource (the answer, evidence, etc.).

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 gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts,' and explicitly says 'prefer ask_pipeworx for casual lookups.' It also highlights the cost trade-off, leaving no ambiguity about selecting this over its sibling.

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