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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses important behavioral traits: it costs an extra LLM call, it refuses rather than fabricates, and it enumerates exact refusal reasons such as not_in_source, no_tool_match, tool_error, data_truncated, and llm_error. This gives an agent a precise mental model of success and failure behavior beyond what annotations convey.

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 and front-loaded with purpose, then return contract, then usage guidance, then cost tradeoff. It is longer than minimal, but nearly every sentence adds real value: routing details, refusal behavior, use cases, and cost. It could be slightly tightened, but it is well-organized and not padded with fluff.

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 query tool with no output schema, the description fully covers the return shape, success and refusal cases, cost behavior, routing behavior, and when to use it over alternatives. The input schema handles parameter details, so nothing critical is missing for correct selection and invocation.

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 baseline is 3. The description does not add meaningful parameter semantics beyond the schema: it only says the question is in natural language, while the schema already describes aliases and natural-language behavior. No parameters are explained in more depth, but the schema is sufficient, so the score stays at baseline.

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 leads with a specific, informative purpose: a 'hallucination-resistant answer mode for high-stakes reads' that routes like ask_pipeworx but extracts answers only from tool results. It clearly distinguishes this grounded tool from the plain ask_pipeworx sibling, leaving no ambiguity about what the tool does or how it differs.

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 gives explicit when-to-use guidance: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' It also names the alternative and the condition to prefer it: 'prefer ask_pipeworx for casual lookups.' This is model usage guidance that directly tells an agent how to choose between siblings.

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