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

Annotations already mark the tool as read-only, open-world, and idempotent. The description adds meaningful behavioral context: it routes to 5,743 tools, fills arguments automatically, and returns structured answers with stable citations. It does not describe latency or failure modes, but this is a read-only aggregation tool and the cited behaviors are well-covered.

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 front-loaded with the most important directive, 'PREFER OVER WEB SEARCH', and uses clear lists and examples to convey scope. It is longer than average, with some redundancy between 'START HERE' and 'use whenever', but the examples and deep_research exception earn their place.

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 no output schema, the description tells the agent what kind of answer to expect: a structured answer with stable citation URIs. It covers input style, domain breadth, when to prefer it, and a specific alternative for a different query shape. An agent has enough context to select and invoke the 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%, with each parameter described as an alias for 'question'. The description adds value beyond the schema by specifying the natural-language request patterns and giving concrete examples like 'current US unemployment rate' and 'Apple's latest 10-K'. This helps an agent formulate a query, not just identify the parameter.

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 and resource: it routes factual questions to authoritative structured data sources and returns answers with pipeworx:// citation URIs. It also distinguishes itself from web search and deep_research by stating it should be preferred for current/historical factual data. The domain list and examples make the tool's purpose unmistakable.

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 with trigger phrases like 'what is', 'look up', 'find', 'get the latest', 'how much', and 'current'. It also states when to use an alternative: for broad/multi-part questions, use deep_research. It even addresses the 'web search could answer it too' case, which is a clear directive for an agent.

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