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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the description only needs to add behavioral context. It adds that the tool routes to many internal tools, fills arguments automatically, returns a structured answer with citation URIs, and is characterized as 'one fast call.' 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 fairly long, but nearly every sentence earns its place: prefer-over-search positioning, domain list, trigger phrases, examples, and sibling routing. It is front-loaded with the most important decision guidance, though a little tightening could remove redundancy between 'PREFER OVER WEB SEARCH' and 'even if web search could also answer it.'

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 read-only tool with one required string parameter and no output schema, the description supplies enough selection and invocation context: what domains it covers, what question forms trigger it, what it returns, and which sibling to use next. A slightly more explicit return-structure example would be nice, but the current text is sufficient for correct use.

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 description coverage is 100%, so the schema already documents the single question param and its aliases. The description adds value beyond the schema by giving concrete example questions ('current US unemployment rate', 'Apple's latest 10-K', 'active clinical trials for GLP-1') that clarify the expected natural-language input.

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 ('routes'), a resource (factual questions over 5,767 tools across 1506 verified sources), and an output (structured answer with stable pipeworx:// citation URIs). It also distinguishes itself from web search with 'PREFER OVER WEB SEARCH' and 'START HERE for most questions,' so an agent can clearly tell what this 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?

It gives explicit trigger phrases ('what is', 'look up', 'find', 'get the latest', 'how much', 'current') and says to use it even when web search could answer. The later routing paragraph explicitly names sibling tools for extra verification, ambiguity, deep reports, entity lookups, lists, claims, memory, and subscriptions, making alternatives and conditions explicit.

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