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

Annotations already declare the tool safe (readOnlyHint, idempotentHint, non-destructive), and the description adds genuinely useful behavioral context: it routes to one of thousands of source tools, returns a structured answer with stable pipeworx:// citation URIs, works on every tier, and makes a single fast call. This goes beyond the structured annotations without contradicting them.

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 long but information-dense and well-structured: opening directive, scope statement, routing behavior, canonical trigger phrases, examples, and escalation guidance all appear in a logical order. Every sentence adds decision-relevant value, though some trimming of the lengthy domain list would not hurt.

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 single-parameter Q&A tool with no output schema, the description covers what the tool does, what kind of questions to ask, what the output looks like (structured answer + citation URIs), how it compares to alternatives, and when to escalate. An agent has everything needed to invoke it correctly and interpret the result.

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 the sole required parameter 'question' is clearly documented with aliases, so the description does not need to add parameter-level detail. The included examples reinforce what kind of question to pass, but they do not materially add beyond the schema, so 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 names a specific, high-value purpose: answering factual questions about real-world data across many domains, and frames itself as the default entry point versus its siblings. It explicitly differentiates from ask_pipeworx_grounded and deep_research by scope and use case, so an agent can select it correctly without inspecting other 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?

It gives explicit trigger phrases ('what is', 'look up', 'find', 'get the latest', 'how much', 'current'), says to prefer it over web search for structured data questions, and names concrete escalation paths: ask_pipeworx_grounded for verbatim evidence and deep_research for broad/multi-part questions. This is model guidance for when-to-use and when-not-to-use.

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