List all Dataplex healthcare data products: CMS provider, facility, nursing home and prior authorization compliance data, FDA adverse events (FAERS/MAUDE), CDC public health statistics, NPPES NPI provider records, hospital and Medicaid spending. Returns slug, name, one-liner, price, and links for each.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosure. It states that the tool returns a list and specifies the exact fields (slug, name, one-liner, price, links), plus the data categories covered. This gives a clear behavioral expectation without side effects or hidden requirements, though it does not mention pagination or access constraints.
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 a single, front-loaded sentence that packs in a detailed list of data categories and return fields without any fluff. Every clause adds specific value, making it appropriately sized and highly scannable.
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 list tool with no parameters and no output schema, the description is fully self-contained: it states exactly what is returned and what product domains are included. It answers the likely questions an agent would have before calling it, and no additional context is needed.
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 tool has zero parameters, and the schema coverage is 100% (vacuously). Since there are no parameters, the description does not need to explain any. The baseline for zero parameters is 4, and the description correctly adds no unnecessary parameter information.
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 uses a specific verb-resource pairing ('List all Dataplex healthcare data products') and enumerates the product categories and return fields. This clearly distinguishes it from siblings like get_product_details or search_health_datasets, which focus on individual products or searching.
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 phrase 'List all' clearly conveys the intended use case: an overview of all available products without filtering or searching. It provides clear context but does not explicitly mention when not to use it or name alternatives, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool has a clearly distinct purpose: list_products and search_health_datasets handle discovery, while the get_* tools target specific aspects of a single product (pricing, dictionary, sample data, access options, trial SQL, full details). Though some tools share overlapping fields, their focused purposes are unambiguous.
All tool names follow a consistent verb_noun pattern in snake_case: get_ for product-specific actions, list_ for enumeration, search_ for querying. This creates a predictable and easily learnable API surface.
8 tools is well-scoped for a healthcare data marketplace. Each tool earns its place, covering discovery, evaluation, and access without unnecessary redundancy or missing essential steps.
The set covers the full lifecycle for a data consumer: discover (list/search), understand (details, dictionary, pricing), evaluate (sample data), and access (access options, trial SQL). No major gaps are apparent for the stated purpose.
