Search Dataplex healthcare datasets by keyword (e.g. 'nursing home compliance', 'prior authorization CMS-0057', 'FDA adverse events', 'NPI provider', 'CDC mortality'). Matches product names, descriptions and table names; returns matching products.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the search scope and return of matching products, but it does not clarify the return format, pagination, or any edge cases. This is minimal behavioral context, not contradictory, but could be more 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 two sentences, with the action verb upfront. It includes illustrative examples and clarifies matching logic without unnecessary padding. Every sentence earns its place, making it concise and well-structured.
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 single-parameter search tool with no output schema, the description provides adequate context on purpose, input semantics, and return type. The main gap is the unspecified format of 'matching products,' but this is a minor omission given the tool's simplicity.
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 schema only defines 'query' as a string, but the description adds meaning by interpreting it as a search keyword and providing concrete examples (e.g., 'nursing home compliance', 'CDC mortality'). This fully compensates for the 0% schema coverage and clarifies the expected input.
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 clearly states the tool's function: searching Dataplex healthcare datasets by keyword, with explicit examples. It specifies the match targets (product names, descriptions, table names) and the return type (matching products), which distinguishes it from sibling tools like list_products or get_product_details.
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 implies usage for keyword-based search through its examples and phrasing, but it does not explicitly state when to use this tool versus alternatives. It lacks mentions of exclusions or comparisons to siblings like list_products, leaving usage guidance mostly implied.
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.
