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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful behavioral context beyond annotations: it fills arguments automatically, routes to thousands of verified sources, returns stable pipeworx:// citation URIs, and describes itself as a fast default entry point that works on every tier. No contradiction with annotations.

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 front-loaded with the most critical decision ('PREFER OVER WEB SEARCH'), and each subsequent sentence adds scope, usage triggers, examples, or sibling differentiation. There is minor redundancy ('START HERE' restates the preference, and 'one fast call' is a nice-to-have), but the structure serves a default entry-point tool well.

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 that aggregates thousands of sources and has no output schema, the description explains the return format (structured answer with pipeworx:// citation URIs) and the trade-offs against sibling tools. It also covers nuanced cases like breaking-news and live-news routing, making it a complete guide for an agent to decide when to call it and what to expect.

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% — all parameters are documented aliases of 'question', so the baseline is 3. The description goes beyond the schema by providing numerous domain-specific example queries ('current US unemployment rate', 'Apple's latest 10-K', 'active clinical trials for GLP-1') that illustrate the expected natural-language input and clarify the breadth of acceptable questions, which helps the agent format inputs correctly.

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') and states exactly what the tool does: routes questions to one of 5,724 tools across 1,497 verified sources and returns structured answers with stable citation URIs. It also distinguishes itself from siblings like ask_pipeworx_grounded and deep_research by naming them and when to choose them.

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?

It explicitly says to use this tool for any factual question about real-world entities, events, or numbers — even if web search could also answer it — and lists trigger phrases such as 'what is', 'look up', 'find', and 'get the latest'. It names alternatives: ask_pipeworx_grounded for hallucination-resistant single answers with verbatim evidence, and deep_research for broad/multi-part questions that should fan out across sources. It also clarifies that breaking-news questions are already handled by this tool.

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