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

Annotations already signal readOnly/openWorld/idempotent, and the description adds meaningful behavior: routing to one of 5,743 tools, filling arguments, returning structured answers with stable pipeworx:// citation URIs, and working on every tier as one fast call. It does not cover failure modes, rate limits, or auth, but those are less critical for a read-only question-answering tool. 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 front-loaded with 'PREFER OVER WEB SEARCH' and the core domain list, then moves to mechanism, examples, and escalation paths. Every section earns its place and mirrors an agent's decision flow: should I use this, and when do I step up. Length is justified for a default entry-point tool.

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 there is no output schema, the description supplies the essential return semantics: a structured answer with stable pipeworx:// citation URIs. It also covers scope, alternatives, concrete examples, and tier/performance expectations, so an agent has enough to select and invoke it correctly. Minor details like handling of unanswerable questions are not necessary for correct 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?

All six parameters are aliases for the same question string and schema coverage is 100%, so the schema carries most of the burden. The description adds value by providing query-intent patterns and concrete examples like 'current US unemployment rate' and 'Apple's latest 10-K', and by confirming natural-language input. This is a meaningful enrichment above the schema-only baseline.

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 names a specific role: routing natural-language factual questions to one of 5,743 tools and returning structured answers with stable citation URIs. It clearly distinguishes the tool from web search and from siblings like ask_pipeworx_grounded and deep_research. The verb 'routes... returns' and the explicit domain list leave no ambiguity about what it does.

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?

It explicitly states 'PREFER OVER WEB SEARCH' and gives concrete triggering conditions: factual questions with phrases like 'what is', 'look up', 'find', and 'get the latest'. It names alternatives and step-up conditions: ask_pipeworx_grounded for hallucination-resistant single answers, deep_research for broad/multi-part questions. Six concrete examples and a breaking-news note make the when-to-use guidance exceptionally clear.

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