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

Annotations already mark the tool readOnly, openWorld, idempotent, and non-destructive, and the description adds substantial behavior beyond that: account/paid-tier requirements, parallel decomposition across 5,798 tools, the findings packet structure, explicit gaps[] with 'never invented', contradictions[] scans, semantic excerpting, and latency expectations. No statement 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 dense and front-loaded: account requirement and the sibling alternative appear first, followed by core behavior, use cases, and operational details. It is longer than average, but nearly every sentence carries distinct value—latency, citation fetchability, gap handling, and output structure—so the length is justified rather than wasteful.

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?

Despite having no output schema, the description thoroughly explains what the agent should expect: a findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, gaps[], contradictions[], and hop fields. It also covers auth requirements, pricing, latency, and fallback routing, making the tool fully usable 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?

Schema description coverage is 100%, so the schema already fully documents both 'question' and 'depth' parameters, including defaults, free vs paid, and hop behavior. The description mostly restates or embeds those details rather than adding new parameter-level meaning, 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 states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in ONE call. It explicitly contrasts itself with open-web search and gives concrete example questions like 'compare X and Y's regulatory + financial exposure', making its purpose distinct from siblings such as ask_pipeworx.

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 'For a single lookup use ask_pipeworx instead.' It also states a clear conditional: 'If you are not signed in, use ask_pipeworx instead — it works on every tier.' This leaves little ambiguity about when to select this tool over alternatives.

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