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

The description discloses that the answer is generated only from tool results, lists the exact success return shape, and enumerates explicit refusal reasons for failure cases. It also warns about the additional LLM call cost. This goes well beyond the readOnly/openWorld/idempotent annotations and does not contradict them.

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 front-loads the core purpose, then delivers the return contract, refusal reasons, usage guidance, and cost comparison without redundant phrasing. Every sentence carries operational value.

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?

Even without an output schema, the description fully specifies success and refusal outputs and names the failure categories. Combined with high schema coverage, an agent has enough information to invoke the tool correctly and decide when to use it instead of ask_pipeworx.

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 schema already documents the question parameter and its aliases. The description adds only indirect context about 'filling arguments' rather than parameter-specific meaning, so the baseline score 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 clearly defines this as a hallucination-resistant answer mode that routes through 5,908 tools and extracts answers only from tool results. It explicitly distinguishes itself from ask_pipeworx and other siblings by naming its exact behavior and output contract.

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 explicitly states when to use this tool: whenever an answer will be quoted, cited, or acted on and facts must not be invented, with concrete examples. It also says to prefer ask_pipeworx for casual lookups and notes the extra LLM call cost, giving an agent a clear decision rule.

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