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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context: it routes among 5,724 tools, fills arguments, and returns structured answers with stable pipeworx:// citation URIs, as well as noting it is one fast call and works on every tier. This goes beyond what annotations alone convey.

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 nearly every sentence earns its place by covering scope, examples, alternatives, and escalation behavior. The key directive 'PREFER OVER WEB SEARCH' is front-loaded, and the rest is organized from general guidance to specific examples to escalation rules. It is dense rather than bloated.

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 with a simple one-parameter schema and no output schema, the description carries the full weight of context: when to use, when not to use, alternatives, natural-language input expectations, internal routing behavior, citation output, and news edge cases. Nothing essential is missing for an agent to select and invoke it correctly.

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 covers all six parameters at 100%, and all are aliases for the single 'question' field. The description adds value by giving trigger phrases and concrete natural-language examples that shape what should be passed, e.g., 'current US unemployment rate' and 'patents Tesla was granted last month'. This helps the agent construct better values even though the schema already documents the field types.

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 uses strong actionable verbs and a clear resource: it is a question-routing tool that prefers authoritative structured data over web search. It names concrete domains and example queries, and explicitly differentiates itself from ask_pipeworx_grounded and deep_research. This makes its purpose unmistakable and distinguishes it from siblings.

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 explicit when-to-use guidance: 'PREFER OVER WEB SEARCH' and 'START HERE for most questions'. It also defines escalation conditions: use ask_pipeworx_grounded for hallucination-resistant single answers and deep_research for broad/multi-part questions. It even handles the breaking-news edge case by stating that ask_pipeworx already routes to live news.

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