snowflake-analytics-mcp-server

Get table sample

get_table_sample

Fetch a sample of rows from a Snowflake table to preview data before running full queries.

Instructions

Fetch a sample of rows from a table. Use this to preview data for a selected table.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "table": "<table>"
    },
    "name": "get_table_sample"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Row limit. Default 10.
`table`	Yes	Table name (unqualified).
`schema`	No	Defaults to SNOWFLAKE_SCHEMA.
`database`	No	Defaults to SNOWFLAKE_DATABASE.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 20, 2026

TDQS

A3.8/5.0

Behavior2/5

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

No annotations are provided, so the description carries full responsibility for disclosing behavioral traits. It only says 'fetch a sample' without revealing specifics like read-only nature, row ordering (random vs. first N), whether the sample is deterministic, or any side effects. This lacks sufficient transparency for an AI agent to safely select and invoke the tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences that directly state purpose and intended use. No filler or redundancy. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple preview tool with full schema coverage and no output schema, the description is nearly sufficient. It could additionally mention the return format (e.g., columns and rows) or clarify that it returns a limited set, but the core behavior is well understood. Given the tool's simplicity, this is adequately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% coverage with descriptions for all four parameters (table, schema, database, limit), including defaults. The description itself adds no extra meaning beyond what the schema states (e.g., it doesn't explain interaction between parameters or formatting). Baseline 3 is appropriate since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Fetch a sample of rows') and the resource ('from a table'), and explicitly frames its purpose as previewing data. This distinguishes it from sibling tools like describe_table (schema) and execute_query (arbitrary SQL), which serve different functions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'Use this to preview data for a selected table' provides explicit guidance on when to use the tool. It does not mention exclusions or alternatives, but the context is clear enough given the sibling tool list. A slightly stronger contrast or when-not-to-use note would push it to 5.

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

Deploy Server