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

Beyond the annotations, the description discloses critical behaviors: it never invents facts, only uses tool result content, returns verbatim evidence, issues explicit refusals with specific refusal_reason values, and costs an extra LLM call. This matches and enriches the readOnly/openWorld/idempotent annotations without contradicting them.

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 but every sentence earns its place: core purpose first, then output/refusal contract, then usage guidance and cost tradeoff. It is front-loaded with the most important differentiators and contains 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, the description fully documents the success response and refusal response shapes, evidence behavior, common refusal reasons, and cost implications. Combined with the annotations and schema coverage, an agent has everything needed to invoke and interpret this tool 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 has 100% description coverage and documents aliases and the natural-language question parameter. The description adds little parameter-specific meaning, instead focusing on tool-level behavior, so a baseline score of 3 is appropriate since the schema already does the heavy lifting.

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 verb-resource pair ('Ask Pipeworx — Grounded') and clearly states it is a hallucination-resistant answer mode that routes across Pipeworx tools, fetches data, and extracts answers only from tool results. It explicitly distinguishes itself from ask_pipeworx by noting the extra LLM call and stronger grounding, so an agent can differentiate it from sibling tools.

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, ask_pipeworx, and tells the agent to prefer that for casual lookups, which fully covers selection behavior.

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