100 REAL rows from a Dataplex product's primary table — the same governed views sold on Snowflake and Databricks. No signup needed. Use this to evaluate schema and content quality.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| max_rows | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses useful behavioral details: no signup required and real data. However, it claims '100 REAL rows' while the max_rows parameter defaults to 25, creating a potential conflict with the schema. This inconsistency reduces clarity about actual tool behavior.
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, front-loaded with the core functionality and value. Every word earns its place, with no superfluous content.
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?
The description covers the main purpose and a key behavioral trait (no signup needed), but it omits parameter definitions and the discrepancy with max_rows is unaddressed. For a simple tool, it is mostly adequate but not fully complete.
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 0%, and the description provides no information about the 'slug' or 'max_rows' parameters. The agent must infer that 'slug' identifies the product, but this is not explicit. The description fails to compensate for the lack of schema documentation.
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: returning 100 real rows from a Dataplex product's primary table. It also specifies the intended use ('evaluate schema and content quality'), distinguishing it from siblings like get_data_dictionary and 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 explicitly states when to use the tool ('Use this to evaluate schema and content quality'). It does not explicitly name alternatives, but the sibling list implies the appropriate contexts for other tools, making the usage context clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
