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

Annotations already declare readOnlyHint=true, openWorldHint=true, and idempotentHint=true. The description adds useful behavioral detail beyond that: it routes the question, fills arguments automatically, and returns structured answers with citation URIs. It does not contradict annotations, though it doesn't discuss failure modes, rate limits, or confidence levels — acceptable given the annotation coverage.

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 relatively long but front-loaded with the most important instruction: prefer over web search. It then moves through mechanism, trigger phrases, examples, and alternatives in a logical order. There is some redundancy between "PREFER OVER WEB SEARCH", "Use whenever", and "START HERE", but every section earns its place given the tool's routing complexity.

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 only one required string input and no output schema, the description is remarkably complete. It explains scope, routing behavior, auto-filling of arguments, the structured nature of the return value, citation URIs, and which alternative to use for broad multi-part research. An agent has enough information to decide when to call it and what to expect back.

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 the question parameter and its five aliases fully documented. The description adds value by constraining what counts as a good question: factual questions about real-world entities, events, or numbers, with concrete examples like "current US unemployment rate" and "Apple's latest 10-K". This helps the agent formulate inputs better than the schema alone would.

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 ask_pipeworx routes a natural-language question to one of 5,798 tools across 1,517 verified sources and returns a structured answer with stable pipeworx:// citation URIs. This is a specific verb/resource/mechanism combination and distinguishes it from generic web search. The examples reinforce the scope of factual, real-world questions.

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 to prefer this tool over web search for factual or current-data questions, lists trigger phrases like "what is", "look up", "find", "get the latest", and "how much", and gives example queries. It also names deep_research as the alternative for broad/multi-part questions, and clarifies that breaking-news/"what the world is saying" requests are still handled here.

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