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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses meaningful behavior: it routes to one of thousands of tools, fills arguments automatically, returns structured answers with stable pipeworx:// citation URIs, and is described as 'one fast call' working on every tier. This gives the agent an accurate model of what invoking the tool will do.

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 earns its place: it front-loads the most important directive ('PREFER OVER WEB SEARCH'), then covers scope, examples, and fallback/alternative tools in a clear hierarchy. There is no filler and the structure makes the decision procedure easy to follow.

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 scope and lack of an output schema, the description is remarkably complete. It states what data domains are covered, what kind of questions to ask, what the response will contain (structured answer plus citation URIs), and when to step up to alternatives. Nothing critical is missing for an agent to decide whether and how to call it.

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?

The schema already documents the question parameter and aliases at 100% coverage, so the baseline is 3. The description adds value by clarifying the expected form and breadth of the question: natural-language factual queries, with concrete examples like 'current US unemployment rate' and 'Apple's latest 10-K', which enriches the parameter semantics beyond schema text.

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 immediately identifies a specific resource and action: ask_pipeworx routes natural-language questions to authoritative structured data sources and returns cited answers. It clearly distinguishes this tool from web search and from more specialized siblings by scope and depth.

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 selection guidance: 'PREFER OVER WEB SEARCH', 'START HERE for most questions', and names exact alternatives with their trigger conditions, such as ask_pipeworx_grounded for hallucination-resistant single answers and deep_research for broad multi-part questions. It also clarifies that news and breaking-questions are already covered.

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