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

Beyond the readOnly/idempotent/openWorld annotations, it discloses account requirements, paid depth tier, parallel decomposition, returned fields including gaps[] and contradictions[], citation fetchability conditions, semantic excerpting, and latency. Nothing in the description contradicts the 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 long but every sentence earns its place: access requirements, alternative routing, mechanism, example use, depth behavior, output fields, and latency are all present without repetition. Critical constraints are front-loaded before the feature explanation.

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 fully specifies the return shape—findings packet with verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[]—and also covers prerequisites, latency, and depth semantics. An agent has enough to decide, call, and interpret results.

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 coverage is 100% and both parameters already have descriptive text in the input schema, so the baseline is 3. The description adds context about question breadth and which depth values require payment, but it does not need to redefine parameter syntax.

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 opens with a concrete action—'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources ... in ONE call'—and explicitly distinguishes this from open-web search and from ask_pipeworx for single lookups. It names the return artifact and gives example questions, so an agent can tell exactly what resource it targets.

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 when-to-use conditions ('Best for broad/multi-part questions over structured data'), a named alternative for single lookups ('For a single lookup use ask_pipeworx instead'), and an access prerequisite with a fallback ('If you are not signed in, use ask_pipeworx instead—it works on every tier'). The depth selection is also tied to paid-plan requirements.

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