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

Annotations already mark it readOnly, openWorld, and idempotent, and the description adds substantial context beyond that: account and plan requirements, parallel decomposition across 5,798 tools, findings packet shape with gaps[], contradictions[], timings, citation resolvability, and a 'never invented' guarantee. This is unusually transparent.

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 dense but front-loaded: account requirement and alternative routing come first, then purpose, then behavioral detail. It earns most of its length, but some citation/packet details are restated, and the paragraph style makes it harder to scan than a bulleted structure would be.

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?

With no output schema, the description carries the burden of explaining return behavior, and it does: evidence, confidence, source, fetched_at, citations, gaps[], contradictions[], and hop semantics. It also covers auth, cost, timing, data scope, and depth trade-offs, so an agent has everything needed to invoke the tool correctly.

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%, so the schema already documents both parameters well, including depth enum semantics and broad/multi-part questions. The description reinforces the paid-plan constraint for thorough and the natural-language question expectation, but adds little new parameter-level meaning beyond what the schema already says.

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 verb and resource: grounded multi-source research across Pipeworx's 1517 structured data sources. It explicitly distinguishes itself from open-web search and from sibling tools like ask_pipeworx, and gives concrete example questions that clarify what it is for.

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 ('Best for broad/multi-part questions over structured data') and explicit alternatives ('For a single lookup use ask_pipeworx instead'). It even handles the auth-gating case: if not signed in, use ask_pipeworx. This is model routing guidance.

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