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

Annotations already cover the safety profile (readOnly, openWorld, idempotent, non-destructive), so the description's job is lighter. It adds meaningful behavioral context: the tool routes to one of 5,743 tools, fills arguments, and returns structured answers with stable pipeworx:// citation URIs. This gives the agent a realistic model of what happens when it is invoked, though it does not discuss potential latency, failure modes, or source conflicts.

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 most, but every part earns its place: usage priority, domain list, delegation behavior, examples, and alternative routing. It is front-loaded with the most important instruction ('PREFER OVER WEB SEARCH'), followed by concrete guidance. Slight redundancy between the domain list and examples prevents a 5.

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 broad router tool with a simple single-parameter schema and no output schema, the description is remarkably complete. It explains what the tool does, when to use it, what it returns, and how to choose between it and close siblings. The examples and alternative guidance cover the key decisions an agent needs to make before invoking this tool.

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 schema already documents the single meaningful parameter (question) plus aliases. The description provides examples and clarifies that natural-language factual questions are expected, but it does not add deeper parameter-level semantics. Baseline 3 is appropriate because the schema carries the load.

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 identifies a specific purpose: routing factual questions about real-world data to appropriate sources and returning structured answers with citation URIs. It names concrete domains (SEC filings, FDA data, FRED/BLS, USPTO, weather, clinical trials, news, stocks, etc.) and explicitly contrasts itself with web search, making its role easy to distinguish from siblings.

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 exceptionally explicit when-to-use guidance: prefer over web search for factual/current/historical questions, with trigger phrases like 'what is', 'look up', 'find', 'get the latest', and multiple worked examples. It also names alternatives (ask_pipeworx_grounded for verbatim evidence, deep_research for broad multi-part questions) and states when those should be chosen instead.

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