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

Annotations already declare read-only, open-world, idempotent, non-destructive. The description goes beyond by disclosing the extra LLM call cost, the explicit refusal structure with specific refusal_reason values, and the fact that answers are extracted only from tool results. This is rich behavioral context that annotations alone do not provide.

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 behavior, routing mechanism, return/refusal format, usage context, and cost tradeoff. Information is front-loaded with the primary purpose stated first, then important output and usage details, then the efficiency comparison. No filler or redundancy.

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, the description fully explains the return shape (success object with answer, evidence, confidence, source, fetched_at) and the refusal object with all possible refusal_reason values. Combined with the use-case guidance and cost note, an agent has everything needed to invoke the tool correctly and interpret its response.

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%, with all six parameters being aliases for 'question' and each described as natural language. The tool description contextualizes the question but adds no parameter-specific semantics beyond what the schema already provides. Baseline 3 is appropriate because the schema fully documents the parameters.

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 clearly states this is a hallucination-resistant answer mode that routes through the same tool selection as ask_pipeworx, fetches data, then extracts an answer using only the tool result. It names the resource (cross-tool grounded answering) and distinguishes itself from siblings by emphasizing evidence-grounded extraction and refusal behavior.

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?

Explicitly states when to use: whenever an answer will be quoted, cited, or acted on and facts must not be invented (financial, legal, medical, public statements). Also names the alternative ask_pipeworx and advises preferring it for casual lookups due to the extra LLM call cost. This gives an agent clear routing guidance.

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