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

Annotations already establish readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the description only needs to add contextual behavior. It adds meaningful details: the tool internally routes to a huge tool database, fills arguments itself, returns structured answers with citation URIs, and is described as 'one fast call' with 'works on every tier'. No contradiction with 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 front-loaded with the most important directive ('PREFER OVER WEB SEARCH') and organized into clear blocks: domain scope, mechanism, triggers, examples, and alternatives. Some redundancy exists between the domain list and the example queries, but every sentence earns its place for a broad default entry point.

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 breadth (general factual question routing, no output schema), the description is remarkably complete. It explains what the tool does, when to prefer it, when to step up to grounded or deep_research, that it handles breaking news, that it works on every tier, and the nature of its output (structured with citation URIs). An agent can confidently invoke it without missing information.

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 already defines 'question' plus aliases, so the baseline is 3. The description adds significant value by giving a rich taxonomy of supported domains (SEC filings, FDA, FRED/BLS, patents, real estate, weather, clinical trials, etc.) and concrete natural-language query examples, which materially helps an agent formulate an effective 'question' parameter beyond the generic 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 states a specific, multi-part behavior: routes the question to one of 5,743 tools, fills arguments, and returns a structured answer with stable pipeworx:// citation URIs. This clearly distinguishes ask_pipeworx from its siblings, which is reinforced by naming ask_pipeworx_grounded and deep_research as different approaches.

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?

Explicit 'PREFER OVER WEB SEARCH' instruction, exhaustive trigger phrases ('what is', 'look up', 'find', 'get the latest', 'how much', 'current'), concrete example queries, and precise step-up guidance: use ask_pipeworx_grounded for hallucination-resistant single answers, deep_research for broad multi-part fan-out, and ask_pipeworx for breaking-news routing. The agent is left with no ambiguity about when to select this tool.

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