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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is established. The description adds meaningful behavioral context: it auto-routes to one of 5,743 tools, fills arguments, and returns structured answers with stable pipeworx:// citation URIs. This goes well beyond the annotations and helps an agent predict how the tool behaves.

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 decision rule and every sentence contributes: directive, mechanism, trigger phrases, examples, and alternatives. It is longer than strictly necessary—especially the list of example questions and repeated emphasis on web search—but for a broad routing tool the extra context is earned.

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 having no output schema, the description explains what the agent should expect back: a structured answer with stable citation URIs. It also gives enough context about when to invoke the tool, what kinds of questions it handles, and which sibling tools to prefer for other needs. Minor edge-case handling is not specified, but this is a strong, self-sufficient definition for a query router.

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 schema already documents the single required question parameter and its aliases. The description adds value by giving concrete example questions and emphasizing that the tool itself fills arguments, reinforcing that natural-language input is expected. It does not add constraints, but none are necessary for this open-ended tool.

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 clear directive—'PREFER OVER WEB SEARCH for questions about current or historical data'—and then explains the mechanism: routes the question to the right one of 5,743 tools and returns a structured answer with citation URIs. It is specific about what the tool does and differentiates it from web search and sibling analysis 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 explicitly says 'Use whenever the user asks...' and provides trigger phrases and concrete examples. It also states 'even if web search could also answer it,' making the preference unambiguous, and names alternatives with conditions: deep_research, bet_research, ask_pipeworx_grounded, or compare_entities for composable analysis, precise function execution, or comparison ground truth.

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