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

Annotations already establish read-only, open-world, idempotent, and non-destructive behavior. The description adds useful operational detail: it routes to the right tool, fills arguments, returns stable citation URIs, works on every tier, and makes a single fast call. This goes beyond the annotation metadata without contradicting it.

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 longer than average but every section earns its place: preference guidance, source coverage, example queries, alternative tools, and sibling disambiguation. It is front-loaded with the most important directive ('PREFER OVER WEB SEARCH') and structured enough for an agent to scan, though it could be tightened without losing meaning.

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 simple single-parameter tool with rich annotations and no output schema, the description is complete: it covers what the tool does, when to use it, when to use alternatives, what kind of input to provide, and what kind of output to expect (structured answers with citations). The example queries cover multiple domains and question phrasings.

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 the question parameter and its aliases at 100% coverage, giving a baseline of 3. The description adds value by providing multiple concrete example questions and clarifying that the input is a natural-language request that the tool will route and fill arguments for, which helps the agent formulate effective queries.

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 (ask/routes) and resource (5,724 tools across 1,497 verified sources) and explains it returns structured answers with citation URIs. It clearly distinguishes itself from siblings like ask_pipeworx_grounded and deep_research by describing its role as the default, fast entry point.

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 to prefer this over web search, declares it the default start point, and gives when-to-use conditions with example query types. It also names step-up alternatives (ask_pipeworx_grounded for hallucination-resistant answers, deep_research for broad multi-part questions), leaving no ambiguity about tool selection.

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