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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses the grounding behavior, the exact success return shape (answer, evidence, confidence, source, fetched_at, refusal_reason), and the refusal mechanism with specific reasons. It also reveals the extra LLM call cost, which is not in annotations. No contradiction with 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 well-structured and front-loaded with the core purpose, followed by routing details, output format, usage guidance, and cost trade-off. Every sentence adds value and is not redundant; it is comprehensive without being verbose.

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?

For a complex tool with routing across 5,892 tools and refusal logic, the description covers the essential aspects: how it works, what it returns, when to use it, and the cost. It even lists refusal reasons and the return shape, making it complete for an agent to decide and 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?

The schema covers all parameters (100% coverage) and describes 'question' and its aliases clearly. The description does not add additional semantics beyond what the schema provides, such as examples or constraints, so the baseline of 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 states a specific verb and resource ('Hallucination-resistant answer mode') and clearly distinguishes it from the sibling ask_pipeworx by describing the extraction step that grounds answers in tool results only. It is unambiguous about what the tool does and how it differs from alternatives.

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 explicitly states when to use this tool ('high-stakes reads, answers that will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups'). It also names the alternative and the cost trade-off (one extra LLM call), giving an agent clear decision criteria.

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