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

Annotations already establish read-only, open-world, idempotent behavior, so the bar is lower. The description adds useful behavioral context: it routes to internal tools, auto-fills arguments, and returns structured citations, which is meaningful beyond the annotations. It does not cover failure modes or latency, but those are not critical given the safety 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 front-loaded with the key instruction to prefer this over web search, which is good. However, the length is substantial and somewhat repetitive—there are multiple restatements of 'factual question,' 'current or historical data,' and 'web search'—and the example list could be trimmed without much loss.

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 complex routing tool with no output schema, the description covers the core expectations: what it routes, what it returns, when to use it, and when to choose an alternative. It does not differentiate from sibling tools like ask_pipeworx_beta or ask_pipeworx_grounded, but overall it is sufficiently complete for effective invocation.

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 real parameter `question` is already well described, so the baseline is 3. The description adds value by specifying the kinds of natural-language inputs that are appropriate, with categories and examples, helping an agent formulate a query correctly.

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 states a specific verb and resource: ask_pipeworx routes a question to the most relevant of 5,908 tools across 1,540 sources, fills arguments, and returns a structured answer with citation URIs. It also distinguishes itself from web search and deep_research, making its niche unmistakable.

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 to prefer this tool over web search for factual and data-oriented questions, lists many concrete trigger phrases and examples, and even names when to use deep_research instead. This gives an agent strong, actionable selection criteria.

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