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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so safety profile is clear. The description adds meaningful behavioral context: the tool acts as a router that fills arguments automatically, searches many sources, and returns stable pipeworx:// citation URIs. This goes beyond the annotations without contradicting them, though it does not mention rate limits or failure modes.

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 front-loaded with the most important directive ('PREFER OVER WEB SEARCH') and then logically builds from scope to examples to alternatives. While verbose, every section serves a distinct purpose—no filler or repetition—and the structure makes key guidance scannable.

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 meta-router tool with no output schema and a single flexible question parameter, the description is remarkably complete. It covers what the tool does, what data domains it serves, how it behaves (routing, auto-fill, citations), when to use alternatives, and provides multiple query examples. Nothing essential for correct invocation is missing.

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 every parameter is documented as an alias for 'question', so the structured data already explains the parameters. The description adds practical semantics by giving sample questions ('current US unemployment rate', 'Apple's latest 10-K') and clarifying that natural-language queries are accepted, which helps the agent formulate valid input.

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 is explicit: it answers factual questions by routing to the right tool among 5,798 across 1,517 verified sources, and returns structured answers with citation URIs. It clearly distinguishes itself from web search and from siblings like ask_pipeworx_grounded and deep_research, so an agent can tell what this tool is for without ambiguity.

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 provides strong when-to-use guidance: prefer over web search for a broad category of data, use for queries starting with 'what is', 'look up', 'find', etc., and includes concrete examples. It also explicitly states when to use alternatives: ask_pipeworx_grounded for verbatim-evidence single answers and deep_research for broad/multi-part questions, leaving no inference to the agent.

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