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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description reveals the exact success and refusal return shapes, including all reason codes (not_in_source, no_tool_match, tool_error, data_truncated, llm_error). It also discloses the extra LLM call cost and the strict grounding behavior of using only tool-result content, which is essential for an agent to trust the output.

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 front-loaded with the core purpose ('Hallucination-resistant answer mode for high-stakes reads') followed by function, return contract, usage guidance, and cost trade-off. Every sentence adds distinct, non-redundant value, and the structure flows logically from what to when to why.

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?

There is no output schema, so the description compensates by fully explaining both success and failure return payloads, including refusal reasons. It also covers the routing mechanism, cost implications, and typical use cases, making the tool self-contained for an agent to decide when and how to call it safely.

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 coverage is 100%, so the input schema already documents all parameters, including aliases for the question param. The description adds no parameter-specific semantics beyond mentioning it fills arguments during routing, but the schema fully carries that burden, meeting the baseline for complete schema documentation.

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 this is a hallucination-resistant answer mode for high-stakes reads, explicitly naming the verb 'ask' and resource 'Pipeworx'. It also distinguishes itself from ask_pipeworx by detailing the additional extraction step and refusal behavior, leaving no ambiguity about what the tool does.

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 provides explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on...' and lists concrete high-stakes domains (financial, legal, medical, public statements). It also names the alternative and gives a clear exclusion: 'prefer ask_pipeworx for casual lookups', plus a cost comparison to aid the routing decision.

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