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

Even though annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, the description adds substantial behavioral context: the answer is extracted only from tool results, refusals are explicit with enumerated reasons, and an extra LLM call is incurred. This gives the agent a clear mental model of what happens when data is absent, which annotations cannot express.

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 information-dense but every sentence earns its place: purpose, routing mechanism, output/refusal shape, use cases, and cost trade-off. It is front-loaded with the core 'grounded' purpose and then builds logically, with 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?

With no output schema present, the description compensates by specifying the full return shape and the possible refusal reasons. It also covers usage context, failure behavior, and cost comparison, making it complete enough for an agent to invoke correctly and interpret results.

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%, and the schema already documents the 'question' parameter plus all aliases (q, query, prompt, text, input). The description adds no parameter-level meaning, but with full schema coverage, the baseline of 3 is appropriate; the schema carries the semantic burden.

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 opens with 'Hallucination-resistant answer mode for high-stakes reads,' which clearly specifies the verb (ask), resource (Pipeworx), and differentiating mode (grounded). It also explicitly contrasts with ask_pipeworx, naming the sibling and explaining the extra LLM call, so an agent can distinguish it without inspecting sibling schemas.

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: whenever an answer will be quoted, cited, or acted on and facts must not be invented, with concrete domains like financial verdicts, legal claims, medical lookups, and public statements. It also states when not to use it: 'prefer ask_pipeworx for casual lookups,' directly naming the alternative.

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