get_snowflake_trial_sql
Copy-paste SQL for an agent INSIDE a Snowflake account to mount a Dataplex listing and start querying trial data in minutes — no browser, no sales call.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Copy-paste SQL for an agent INSIDE a Snowflake account to mount a Dataplex listing and start querying trial data in minutes — no browser, no sales call.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It discloses that the tool returns SQL for mounting a Dataplex listing and enables quick trial data querying. However, it lacks details on prerequisites like required Snowflake privileges or what the SQL includes, so it provides limited behavioral insight.
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, readable sentence that front-loads the core action. The phrase 'no browser, no sales call' adds a bit of promotional flair but doesn't waste significant space. Slightly trimmed would earn a 5.
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 tool is simple with one parameter and no output schema or annotations. The description explains the overall purpose and usage context, but the missing parameter semantics and lack of any return format details mean an AI agent would struggle to invoke it correctly without further info.
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 does not explain the 'slug' parameter at all. The agent is given no guidance on what value to provide or how it affects the returned SQL, making this parameter effectively undocumented.
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: providing copy-paste SQL to mount a Dataplex listing and query trial data from within Snowflake. It specifies the verb, resource, and scope, and distinguishes itself from siblings by focusing on Snowflake-specific SQL setup.
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 clear context for when to use: for an agent inside a Snowflake account, with no browser or sales call needed. However, it doesn't explicitly exclude alternatives or name sibling tools, so it falls short of a perfect score.
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.