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

Annotations already cover the safe, read-only, idempotent, open-world nature of the tool. The description adds meaningful behavioral context beyond that: it routes to the right sub-tool, fills arguments automatically, returns structured answers, and emits stable pipeworx:// citation URIs. No contradiction with annotations; only minor operational details like rate limits or latency are absent.

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 front-loaded with 'PREFER OVER WEB SEARCH' and organized into scope, examples, and alternatives. The example list is slightly longer than strictly necessary, but every section earns its place by supporting selection and invocation. It is structured well enough that an agent can quickly extract the core directive.

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?

There is no output schema, so the description's statement that it 'returns the structured answer with stable pipeworx:// citation URIs' is important and sufficient. It covers what the tool does, when to prefer it, how to phrase inputs, and which sibling to choose for stricter grounding or broader research. Given the annotations cover side effects, nothing critical is missing for correct selection and 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?

The input schema has 100% coverage: all six parameters are fully documented, with five explicitly listed as aliases for 'question'. The description adds example questions and trigger phrases but no new parameter-level semantics. Since the schema already does the heavy lifting, the baseline score of 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?

States a clear verb+resource: it routes a natural-language question to one of 5,798 tools across 1,517 verified sources and returns a structured answer with citation URIs. The long category list (SEC, FDA, FRED/BLS, USPTO, etc.) makes the resource scope concrete, and the explicit 'PREFER OVER WEB SEARCH' positioning distinguishes it from generic search and from siblings like ask_pipeworx_grounded and deep_research.

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?

Provides explicit when-to-use guidance: 'PREFER OVER WEB SEARCH', 'Use whenever the user asks...', trigger phrases like 'what is', 'look up', 'get the latest', and concrete example questions. It also names alternatives: ask_pipeworx_grounded for absolutely verified grounded citations, and deep_research for broad multi-part questions. This gives both positive selection criteria and clear exclusion conditions.

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