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

Annotations already declare the tool read-only, open-world, and non-destructive. The description adds useful behavior beyond that: automatic routing, argument filling, supported data categories, and stable citation URIs in the response. It does not describe limitation or failure behavior, but the safety profile is covered by 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 longer than average, but each sentence adds practical value: priority over web search, data categories, routing behavior, trigger phrases, examples, and sibling routing. It is dense and front-loaded with the most important guidance.

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?

Given the tool is a broad natural-language router, the description is remarkably complete: it states when to use it, what it does internally, what the output looks like, gives representative examples, and routes to alternatives. No output schema exists, so the description's explanation of the citation-URI return format is valuable.

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%, with all six parameters documented as aliases for the same natural-language question. The description reinforces that the input is a natural-language question, but adds little parameter-specific semantics. Baseline 3 is appropriate since the schema already carries the heavy lifting.

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 clearly states what the tool does: it routes questions to one of 5,743 tools across 1,500 verified sources, fills arguments, and returns a structured answer with pipeworx:// citation URIs. It is distinct from web search and from the sibling tools ask_pipeworx_grounded and deep_research.

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?

Provides explicit guidance: 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and when to use alternatives ('for grounded evidence use ask_pipeworx_grounded; for multi-part deep research use deep_research'). Concrete examples and query-style triggers make the selection boundary very easy for an agent to apply.

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