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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context beyond this: the tool routes to the correct one of 5,929 tools, fills arguments, returns a structured answer with stable pipeworx:// citation URIs, works on every tier, and is one fast call. 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 longer than average but every section earns its place: routing primacy, domain list, example queries, tier/performance note, and sibling escalations are all densely packed. It is front-loaded with the most important instruction (prefer over web search) and then layers supporting detail, though some repetition ('START HERE', 'default entry point') could be trimmed.

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, general-purpose question-answering tool with no output schema, the description covers the key operational needs: when to use it, what it does internally, what the response shape is (structured answer with citation URIs), and when to escalate to alternatives. It lacks explicit failure-mode or limitation disclosure, but the combination of rich annotations and the tool's broad scope makes this adequate.

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 description coverage is 100% and all six parameters are documented aliases of 'question', so the schema already carries the semantic weight. The description adds example queries and clarifies the accepted natural-language input, but it does not need to compensate for schema gaps; baseline 3 is appropriate.

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+resource: routes a natural-language question to the correct tool among 5,929 verified sources and returns a structured answer with citations. It explicitly distinguishes itself from siblings like ask_pipeworx_grounded and deep_research, so an agent can tell them apart without opening schemas.

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 when-to-use guidance: prefer this over web search for factual questions about real-world entities, events, or numbers. It also names concrete alternatives and step-up conditions (ask_pipeworx_grounded for verbatim evidence+confidence, deep_research for broad/multi-part questions), and even mentions exclusions like breaking-news routing.

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