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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds useful behavioral context: it routes across 5,801 tools, fills arguments, returns stable citation URIs, and is a fast default entry point. No contradictions 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 longer than minimal but well-structured with use cases, examples, and alternative-tool guidance. Some repetition exists around 'use whenever' and examples, but overall the length is justified by the tool's broad router role.

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 general-purpose question router, the description covers domains, invocation triggers, expected output format, fallback alternatives, and limitations. It is complete enough for an agent to select and call the tool correctly without additional context.

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% and the description adds example questions, but the parameter semantics are essentially already captured by the schema's alias explanations. The description does not meaningfully enrich the parameter meanings beyond the schema.

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?

Description clearly states the tool routes natural-language questions to the appropriate authoritative data source and returns structured answers with citations. It also distinguishes itself from web search and sibling tools like 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 'prefer over web search' guidance, lists specific domains and question phrasings, and gives concrete escalation paths for when to use grounded or deep research alternatives. This leaves little ambiguity about when to invoke the tool.

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