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

The description goes well beyond the readOnly/idempotent annotations by detailing the internal flow (routing across 5,908 tools, filling arguments, fetching data), the exact success/refusal return shapes, and the full list of refusal reasons. It also discloses the extra LLM call cost, which is relevant 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 dense but every sentence earns its place: purpose, mechanism, return/refusal contract, when-to-use, and cost trade-off are all packed in a compact block. It is front-loaded with the core value proposition and contains no filler.

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 natural-language lookup tool with no output schema, the description sufficiently covers return values, refusal semantics, use cases, and cost. The explicit refusal_reason list is particularly valuable for agents deciding whether the answer is trustworthy.

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 baseline is 3. The description adds meaningful context by clarifying that routing and tool selection are automatic and that the question is in natural language ('picks the right tool... fills arguments'), which helps the agent understand its own role in filling the parameters. This goes beyond the schema's basic parameter names.

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 names a specific, distinctive behavior: 'Hallucination-resistant answer mode for high-stakes reads' that extracts answers using only tool results, with evidence and explicit refusals. It clearly differentiates itself from the sibling ask_pipeworx by emphasizing groundedness and evidence extraction.

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 the tool ('whenever an answer will be quoted, cited, or acted on... must not invent facts') and when to prefer the sibling ask_pipeworx ('casual lookups'), including the trade-off of an extra LLM call. This is direct, actionable guidance.

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