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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses substantial behavior: it routes to a large toolset, fills arguments automatically, returns structured answers with stable citation URIs, works on every tier, and is a single fast call. This gives the agent a clear picture of how the tool operates and what kind of response to expect, well beyond what the annotations declare.

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 critical 'PREFER OVER WEB SEARCH' directive and explicitly structured around when to use this tool versus alternatives. There is some redundancy in listing data categories, trigger phrases, and examples, but each section adds routing value. It is not bloated enough to obscure the main message.

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 high-complexity routing tool with no output schema, the description is remarkably complete: it names the data domains, gives usage triggers, provides examples, describes the output (structured answer with citation URIs), and clearly distinguishes sibling tools. There is no critical missing guidance for an agent deciding whether and how to call it.

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?

The schema already documents all six parameters as aliases at 100% coverage, so the baseline is a 3. The description adds value on top by clarifying the semantic range of acceptable questions with trigger phrases and concrete examples ('current US unemployment rate', 'Apple's latest 10-K', 'patents Tesla was granted last month'), which helps an agent craft effective inputs beyond mere schema compliance.

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 is explicit about what the tool does: it routes natural-language questions to the right one of 5,724 tools across 1,497 verified sources and returns structured answers with stable citation URIs. It clearly distinguishes itself from siblings by naming ask_pipeworx_grounded and deep_research and explaining how those differ, so an agent can tell them apart without inspecting schemas.

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 strong when-to-use guidance: prefer over web search for factual questions, start here for most questions, and step up to ask_pipeworx_grounded for hallucination-resistant evidence or deep_research for broad multi-part questions. It also covers breaking news routing and explicitly says web-search-answerable questions are still appropriate, leaving little ambiguity.

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