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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds meaningful context beyond these: it routes the question among thousands of tools, fills arguments automatically, and returns structured answers with stable pipeworx:// citation URIs. This gives the agent a realistic 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 longer than average, but most of the length serves the legitimate need of routing among many sibling tools. It is front-loaded with the most important instruction ('PREFER OVER WEB SEARCH'), then covers behavior, example queries, and alternative tools. Some repetition exists between the initial domain list and later examples, but nothing is purely filler.

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 routing complexity, lack of an output schema, and the large sibling set, the description is complete: it explains what the tool does, how it should be used, what it returns, when not to use it, and which alternatives to choose. An agent has enough context to select and invoke this tool correctly without additional inference.

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 input schema already documents the 'question' parameter and its aliases with 100% coverage. The description adds value by detailing what kinds of natural-language questions are appropriate, providing concrete examples, and clarifying that even factual questions answerable by web search are valid inputs. This goes beyond the baseline for high schema coverage.

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 identifies ask_pipeworx as a semantic router that answers factual questions and returns structured data with pipeworx citation URIs. It explicitly contrasts itself against web search and names sibling tools like deep_research, resolve_entity, ask_pipeworx_beta, and ask_pipeworx_grounded, so an agent can distinguish it from alternatives.

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 guidance on when to prefer this tool over web search, when to use it for factual lookups, and when to use specific siblings instead: deep_research/resolve_entity for broad exploration or nuanced judgment, ask_pipeworx_beta for high-volume queries, and ask_pipeworx_grounded when traceability to the returned answer is required. This is strong alternative-selection guidance.

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