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

Annotations already establish readOnly, idempotent, and non-destructive behavior, so the bar is lower. The description adds valuable behavioral context beyond annotations: it routes to one of 5,743 tools, fills arguments, returns a structured answer with stable pipeworx:// citation URIs, works on every tier, and is a fast default entry point. It does not add details about rate limits or authentication, but that is not a significant gap given the existing 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 long, but it is front-loaded with the most important guidance ('PREFER OVER WEB SEARCH') and structured with examples and explicit escalation rules. Most sentences earn their place by answering when, why, and how to use the tool. It could be tightened slightly, but the density of useful guidance justifies the length.

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 single-free-text-parameter tool with no output schema, the description is remarkably complete: it states the input type, gives examples, explains the routing behavior, describes the output format (structured answer with citation URIs), and clarifies when to step up to sibling tools. An agent has everything needed to invoke this 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%, with the schema already describing 'question' and its aliases. The description goes beyond schema by clarifying the semantic range of acceptable input with examples like 'current US unemployment rate', 'Apple's latest 10-K', and 'active clinical trials for GLP-1'. This helps the agent formulate a good natural-language query even though no parameter-specific syntax details are added.

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 that this tool answers factual real-world questions by routing to a large set of verified sources, and it differentiates itself from siblings like ask_pipeworx_grounded and deep_research. The verb-resource pair is clear: 'ask Pipeworx' for structured data questions, prefer over web search. The inclusion of concrete example queries makes the scope unmistakable.

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 gives explicit trigger phrases ('what is', 'look up', 'find', 'get the latest') and says to prefer this over web search. It also names the exact escalation path: use ask_pipeworx_grounded for hallucination-resistant single answers and deep_research for broad multi-part questions. This is model usage guidance with clear alternatives and selection conditions.

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