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

Annotations already establish readOnly, idempotent, open-world, and non-destructive behavior. The description adds meaningful behavioral context beyond this: it routes through a large corpus of sources, fills arguments automatically, and returns structured answers with stable pipeworx:// citation URIs. It also notes it is 'one fast call' and 'works on every tier,' which helps an agent set expectations.

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?

Although lengthy, the description is front-loaded with the most important directive and every sentence earns its place: scope, triggers, examples, alternatives, and news-handling clarification. The structure moves from broad guidance to specific examples to escalation paths, making it easy for an agent to parse and act on.

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 high-complexity, default entry-point tool, the description is remarkably complete: it states the tool's scope, routing behavior, citation return format, trigger scenarios, examples, and when to switch to sibling tools. Even without an output schema, it tells the agent what kind of answer to expect, leaving no critical gap for invoking it correctly.

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 the single required parameter is well described, including aliases. The description adds trigger-phrase examples and illustrative questions, but these primarily reinforce usage rather than add new parameter-level semantics. Baseline 3 is appropriate since the schema already carries the parameter documentation burden.

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 opens with a precise directive: 'PREFER OVER WEB SEARCH for questions about current or historical data,' naming the exact resource domains and the routing behavior. It clearly identifies the tool as an aggregator that 'routes the question to the right one of 5,718 tools' and returns cited answers, distinguishing it from siblings like ask_pipeworx_grounded and deep_research.

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 provides explicit when-to-use guidance: 'Use whenever the user asks ... or any factual question about real-world entities, events, or numbers.' It also names alternatives and the conditions for stepping up: ask_pipeworx_grounded for hallucination-resistant single answers and deep_research for broad/multi-part questions. This is exceptionally actionable.

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