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

Annotations already declare the tool read-only, idempotent, open-world, and non-destructive. The description adds meaningful behavioral context: it routes to one of 5,743 tools across 1,500 sources, fills arguments, returns pipeworx:// citation URIs, works on every tier, and is a single fast call. It could disclose more about response variations or edge cases, but it is transparent about the core behavior.

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 long but dense and front-loaded with the most important guidance ('PREFER OVER WEB SEARCH'). The examples and escalation paths earn their place, though there is slight redundancy around it being the default entry point. Overall it is structured and readable for an agent.

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 tool with a single natural-language input, the description covers the full context: what domains it handles, what query patterns to use, concrete examples, how it differs from alternatives, and what the answer looks like (structured answer with citation URIs). No additional information is needed for an agent to select and invoke 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?

Schema coverage is 100% and the question parameter is already well documented with aliases. The description adds value beyond the schema by clarifying the kinds of natural-language queries that work ('what is', 'look up', 'current', etc.) and by giving concrete examples like 'current US unemployment rate' and 'Apple's latest 10-K', which helps an agent shape the input appropriately.

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 the tool's function: it answers factual questions by routing them to the appropriate one of thousands of verified data tools and returns a structured answer with citation URIs. It also differentiates itself from siblings like ask_pipeworx_grounded and deep_research, so an agent can tell what this specific tool is for.

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: prefer over web search for factual, current, or historical data; use as the default entry point for most questions. It also names specific alternatives for escalation: ask_pipeworx_grounded for hallucination-resistant single answers and deep_research for broad/multi-part questions, with a note that breaking news is already handled here.

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