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

Beyond the readOnly/idempotent annotations, the description discloses a detailed refusal mechanism with specific refusal_reason values, notes an extra LLM call cost, and explains that answers are derived strictly from tool results. This equips the agent to handle failure modes like 'no_tool_match' or 'data_truncated' without guessing.

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?

Each of the four sentences earns its place: purpose, routing mechanism, return shape, and usage guidance. The core classification is front-loaded, and the refusal enum is compactly presented in a way that avoids a separate output schema.

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?

Despite the absence of an output schema, the description fully documents success and refusal return shapes, refusal reasons, cost tradeoff, and example domains. The tool is self-contained for an agent to invoke and interpret correctly.

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?

The input schema already covers 100% of parameters, documenting 'question' and its aliases (q, text, input, query, prompt). The description adds no additional parameter-level meaning beyond referring to natural language questions, so it sits at the high-schema-coverage 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 opens with a precise phrase 'Hallucination-resistant answer mode' and explains it 'EXTRACTS the answer using ONLY what the tool result contains', clearly stating the tool's purpose. It also distinguishes itself from the sibling ask_pipeworx by noting 'Same routing' but adding grounded extraction and refusal behavior, making the differentiation explicit.

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 explicitly states when to use this tool: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' and when not to: 'prefer ask_pipeworx for casual lookups.' It also provides concrete examples of high-stakes domains, leaving no ambiguity about selection.

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