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

Beyond the readOnly/openWorld/idempotent annotations, the description reveals refusal behavior and all refusal reasons (not_in_source, no_tool_match, tool_error, data_truncated, llm_error), the evidence/verbatim quote contract, confidence reporting, and the extra LLM call cost. There is no contradiction with the 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 long but dense and front-loaded: capability, mechanism, return contract, refusal contract, use cases, and tradeoff. Every sentence earns its place and there is no filler or repetition.

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, it fully enumerates the success return shape and refusal shape, names concrete high-stakes use cases, and identifies the cheaper alternative. An agent has enough information to invoke the tool correctly and interpret its results in context.

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 parameters and their aliases. The description adds that routing follows 'ask_pipeworx,' which gives context for the question parameter, but it does not need to and does not substantially expand on the schema.

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 mode ('Hallucination-resistant answer mode for high-stakes reads'), states what it does (routes to a tool, fetches data, extracts the answer only from the result), and differentiates it from ask_pipeworx by noting the grounded extraction and extra cost. It is a specific verb-plus-resource description that distinguishes it from siblings.

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: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also gives the exclusion and alternative: 'prefer ask_pipeworx for casual lookups' and 'Costs one extra LLM call vs ask_pipeworx.' This is clear when/when-not/alternative guidance.

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