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Snowflake MCP Server

by ncejda-g2
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Python
Remote

Describe Table

describe_table

Get detailed column information for any table to understand its structure and write precise SQL queries.

Instructions

Get detailed column information for a specific table.

USE THIS WHEN: You need column names, types, and constraints to write a query.
Like SQL's: DESCRIBE TABLE database.schema.table

RETURNS: Detailed column information
- For each column: name, data_type, nullable, position, default, comment, is_primary_key

HOW IT WORKS:
- Looks up table in cache; fetches column details on-demand if not yet loaded
- First call for a table queries Snowflake live (~200ms), subsequent calls use cache
- If table not in cache at all, returns error (use show_tables or find_tables first)

Note: To get sample data rows, use execute_query tool separately.

Parameters:
- database: Database name
- schema: Schema name
- table: Table name

Examples:
- describe_table("SALES_DB", "PUBLIC", "CUSTOMERS")
- describe_table("GDC", "STAGING", "ADMIN__CATEGORIES")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYes
schemaYes
databaseYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv1.0.3
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / database / title
      Removed value: -"Database"
    • removedInput schema / properties / schema / title
      Removed value: -"Schema"
    • removedInput schema / properties / table / title
      Removed value: -"Table"
  2. Changed1 schema field changedv1.0.0
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "type": "object"
-}New value: +null
  3. First observedv0.2.3

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral disclosure burden, and it does so thoroughly. It explains the cache lookup, on-demand fetch, ~200ms live query on first call, subsequent cache use, and the error condition when the table is not cached. It also clarifies this is a metadata retrieval operation rather than a data query.

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 well-structured with clear headings: purpose, when to use, returns, how it works, parameters, and examples. It is detailed but every section earns its place and contributes actionable information, with the core purpose front-loaded at the top.

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

Completeness5/5

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

For a simple 3-parameter metadata tool with no annotations and no output schema, this description is remarkably complete. It covers return fields, performance/caching behavior, error conditions, prerequisites, and the correct alternative for data rows, so an agent has everything needed to invoke it correctly.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate, and it does by naming each parameter (database, schema, table) and providing two realistic examples. It adds meaning beyond the bare string types in the schema, though it stops short of extra details like case sensitivity or fully-qualified name formatting.

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 opens with a specific action and resource: 'Get detailed column information for a specific table.' It clearly distinguishes itself from siblings by explicitly directing users to show_tables/find_tables for table discovery and execute_query for sample data, so an agent can tell exactly what this tool is and is not for.

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

Usage Guidelines5/5

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

The description includes an explicit 'USE THIS WHEN' section: when column names, types, and constraints are needed to write a query. It also warns that tables not in cache will error and that show_tables/find_tables should be called first, providing clear when-to-use and when-not-to-use guidance relative to alternatives.

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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