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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: the tool routes to one of 5,728 tools, fills arguments automatically, and returns a structured answer with stable pipeworx:// citation URIs. It also notes the distinction between a single grounded answer and deep_research's multi-source synthesis. It does not discuss failure modes or non-answer cases, but given annotation coverage, the description goes beyond basic disclosure.

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 information-dense and well front-loaded: the first sentence immediately tells the agent to prefer this over web search. The domain list, trigger phrases, and examples are purposeful and support tool selection. Slight redundancy in the closing sentence about news feeds, but overall every section earns its place.

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?

With no output schema, the description compensates by clearly describing the return style: structured answer with stable pipeworx:// citation URIs. It also covers routing behavior, source scope (1,498 verified sources), and sibling alternatives for non-factual or broad questions. It doesn't detail ambiguous-question handling or rate limits, but for a one-parameter natural-language tool with rich annotations, the description is nearly complete.

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 schema documenting the 'question' parameter and all aliases (q, text, input, query, prompt). The description adds semantic value by explaining that the input is a natural-language question and providing multiple concrete example questions that illustrate the expected phrasing and scope. It doesn't need to explain each parameter since the schema already does so thoroughly.

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 states a specific verb and resource: ask_pipeworx is a routing tool that answers factual questions with authoritative structured data and citations. It enumerates concrete domains (SEC, FDA, FRED/BLS, patents, real estate, etc.) and distinguishes itself from web search and sibling tools like deep_research. The examples ('current US unemployment rate', 'Apple's latest 10-K') anchor the purpose clearly.

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 'PREFER OVER WEB SEARCH' and gives when-to-use guidance: factual questions about real-world entities, events, or numbers, even if web search could answer them. It also names alternatives: ask_pipeworx_grounded for grounded answers and deep_research for broad multi-part questions. Trigger phrases like 'what is', 'look up', 'find', 'get the latest', 'how much' are provided, making selection unambiguous.

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