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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context beyond those annotations: it routes across 5,738 tools and 1,499 verified sources, fills arguments automatically, and returns structured answers with stable citation URIs. It does not discuss failure modes or fallback behavior, but the annotation coverage lowers the burden.

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 every sentence carries routing value: the opening establishes preference over web search, the middle defines scope and examples, and the end explicitly routes to the correct sibling tools. Key directives like PREFER OVER WEB SEARCH and START HERE are front-loaded or emphasized, and the examples concretely demonstrate acceptable inputs.

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's broad router nature, no output schema, and multiple sibling tools, the description covers what the tool does, when to use it, what kind of input to provide, and what kind of output to expect. It stops short of describing exact output formatting, citation structure details, or behavior when no source can answer, but it is sufficiently complete for correct selection and invocation.

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%, with all six parameters documented as aliases for the natural-language question. The description adds helpful example questions but does not add parameter-level guidance such as length limits, language constraints, or how to structure complex queries. This is adequate given the schema already covers the parameter semantics.

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 that this tool routes factual questions about current or historical real-world data to the appropriate source tool and returns structured answers with pipeworx:// citation URIs. It provides concrete categories, trigger phrases, and examples, and differentiates itself from siblings by positioning itself as the default starting point over web search.

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 when to prefer ask_pipeworx over web search and provides specific routing alternatives: resolve_entity for simple lookups, compare_entities for entity comparison, ask_pipeworx_grounded for a single grounded answer, and deep_research for research reports. It even handles the breaking-news edge case, leaving little to inference.

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