snowflake-analytics-mcp-server

Describe table

describe_table

Retrieve column names, data types, nullability, and defaults for a Snowflake table or view. Specify the table name to inspect its schema structure and column metadata.

Instructions

Return column names, data types, nullability and defaults for a table (or view).

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": "describe_table"
  }
}

Input Schema

Name	Required	Description
`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

A4/5.0

Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It clearly states the operation returns specific metadata and applies to views as well. While it doesn't explicitly mention it's read-only, the 'Return' framing and the absence of side effects make it clear.

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?

The description is a single, concise sentence that immediately states the tool's function without any redundant words or filler. It is well-structured and front-loaded with the core action.

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 read-only metadata tool with a fully described schema, the description adequately covers the purpose and output. It could mention permissions or error behavior, but those are not critical for an agent to invoke this tool correctly in most cases.

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 provides 100% coverage of all three parameters, each with descriptive names and defaults. The description adds minimal extra meaning beyond the schema, but it does reinforce the purpose of the parameters (referring to a table or view). Baseline 3 is appropriate given high schema coverage.

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 tool returns column metadata (names, types, nullability, defaults) for a table or view. It uses a specific verb ('Return') and distinguishes itself from sibling tools like list_tables or get_table_sample by focusing on schema introspection.

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

Usage Guidelines3/5

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

The description implies the tool is for inspecting table structure, but it does not explicitly state when to use it over alternatives like list_tables or get_table_sample. There is no 'when to use' or 'when not to use' guidance, so usage context is only inferred from the purpose.

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

Deploy Server