Snowflake 管理的 MCP 服务器

概述

Note

Snowflake supports Model Context Protocol revision 2025-11-25.

Model Context Protocol (MCP), is an open-source standard (https://modelcontextprotocol.io/docs/getting-started/intro) that lets AI agents securely interact with business applications and external data systems, such as databases and content repositories. MCP lets enterprise businesses reduce integration challenges and quickly deliver outcomes from models. Since its launch, MCP has become foundational for agentic applications, providing a consistent and secure mechanism for invoking tools and retrieving data.

The Snowflake-managed MCP server lets AI agents securely retrieve data from Snowflake accounts without needing to deploy separate infrastructure. You can configure the MCP server to serve Cortex Analyst, Cortex Search, and Cortex Agents as tools, along with custom tools and SQL executions on the standards-based interface. MCP clients discover and invoke these tools, and retrieve data required for the application. With managed MCP servers on Snowflake, you can build scalable enterprise-grade applications while maintaining access and privacy controls. The MCP server on Snowflake provides:

  • 标准化集成： 用于工具发现和调用的统一接口，符合快速发展的标准。
  • 全面身份验证： 利用 Snowflake 的内置 OAuth 服务，为 MCP 集成启用基于 OAuth 的身份验证。
  • 稳健的治理： 对 MCP 服务器及工具实施基于角色的访问控制 (RBAC)，用于管理工具发现与调用。

For information about the MCP lifecycle, see Lifecycle (https://modelcontextprotocol.io/specification/2025-11-25/basic/lifecycle). For an example of an MCP implementation, see the Getting Started with Managed Snowflake MCP Server (https://quickstarts.snowflake.com/guide/getting-started-with-snowflake-mcp-server/index.html) Quickstart.

MCP server security recommendations

Important

When you configure hostnames for MCP server connections, use hyphens (-) instead of underscores (_). MCP servers have connection issues with hostnames containing underscores.

Using multiple MCP servers without verifying tools and descriptions could lead to vulnerabilities such as tool poisoning or tool shadowing. Snowflake recommends verifying third-party MCP servers before using them. This includes any MCP server from another Snowflake user or account. Verify all tools offered by third-party MCP servers.

We recommend using OAuth as the authentication method. Using hardcoded tokens can lead to token leakage.

When using a Programmatic Access Token (PAT), set it to use the least-privileged role allowed to work with MCP. This will help prevent leaking a secret with access to a highly-privileged role.

Configure proper permissions for the MCP server and tools following the least-privilege principle. Access to the MCP Server does not give access to the tools. Permission needs to be granted for each tool.

创建 MCP 服务器对象

创建一个对象，指定相关工具及其他元数据。经过必要的身份验证后，连接到 MCP 服务器的 MCP 客户端能够发现并调用这些工具。

  1. 导航到要在其中创建 MCP 服务器的数据库和架构。
  2. 创建 MCP 服务器： 
    CREATE [ OR REPLACE ] MCP SERVER [ IF NOT EXISTS ] <server_name>
  FROM SPECIFICATION $$
 tools:
   - name: "product-search"
     type: "CORTEX_SEARCH_SERVICE_QUERY"
     identifier: "database1.schema1.Cortex_Search_Service1"
     description: "cortex search service for all products"
     title: "Product Search"

   - name: "revenue-semantic-view"
     type: "CORTEX_ANALYST_MESSAGE"
     identifier: "database1.schema1.Semantic_View_1"
     description: "Semantic view for all revenue tables"
     title: "Semantic view for revenue"
  $$

Snowflake currently supports the following tool types:

  • CORTEX_SEARCH_SERVICE_QUERY: Cortex Search Service tool
  • CORTEX_ANALYST_MESSAGE: Cortex Analyst tool
  • SYSTEM_EXECUTE_SQL: SQL execution
  • CORTEX_AGENT_RUN: Cortex Agent tool
  • GENERIC: tool for UDFs and stored procedures

The following examples show how to configure different tool types:

Using the Analyst tool, your client can generate SQL from natural language text. Use the following code to specify the tool configuration.

Note

The Snowflake-managed MCP server only supports using semantic views with Cortex Analyst. It does not support semantic models.

tools:
  - name: "revenue-semantic-view"
    type: "CORTEX_ANALYST_MESSAGE"
    identifier: "database1.schema1.Semantic_View_1"
    description: "Semantic view for all revenue tables"
    title: "Semantic view for revenue"

Use the following examples to create and configure custom tools using UDFs and stored procedures:

The following examples demonstrate creating UDFs that can be used as custom tools:

-- create a simple udf
CREATE OR REPLACE FUNCTION MULTIPLY_BY_TEN(x FLOAT)
RETURNS FLOAT
LANGUAGE PYTHON
RUNTIME_VERSION = '3.8'
HANDLER = 'multiply_by_ten'
AS
$$
def multiply_by_ten(x: float) -> float:
  return x * 10
$$;

SHOW FUNCTIONS LIKE 'MULTIPLY_BY_TEN';

-- test return json/variant
CREATE OR REPLACE FUNCTION CALCULATE_PRODUCT_AND_SUM(x FLOAT, y FLOAT)
RETURNS VARIANT
LANGUAGE PYTHON
RUNTIME_VERSION = '3.8'
HANDLER = 'calculate_values'
AS
$$
import json

def calculate_values(x: float, y: float) -> dict:
  """
  Calculates the product and sum of two numbers and returns them in a dictionary.
  The dictionary is converted to a VARIANT (JSON) in the SQL return.
  """
  product = x * y
  sum_val = x + y

  return {
      "product": product,
      "sum": sum_val
  }
$$;

-- test return list/array
CREATE OR REPLACE FUNCTION GET_NUMBERS_IN_RANGE(x FLOAT, y FLOAT)
RETURNS ARRAY -- Use ARRAY to explicitly state a list is being returned
LANGUAGE PYTHON
RUNTIME_VERSION = '3.8'
HANDLER = 'get_numbers'
AS
$$
def get_numbers(x: float, y: float) -> list:
  """
  Returns a list of integers between x (exclusive) and y (inclusive).
  Assumes x < y.
  """
  # Ensure x and y are treated as integers for range generation
  start = int(x) + 1
  end = int(y) + 1 # range() is exclusive on the stop value

  # Use a list comprehension to generate the numbers
  # The Python list will be converted to a Snowflake ARRAY.
  return list(range(start, end))
$$;

  1. To show MCP servers, use the following commands:

    SHOW MCP SERVERS IN DATABASE <database_name>;
SHOW MCP SERVERS IN SCHEMA <schema_name>;
SHOW MCP SERVERS IN ACCOUNT;

    The following shows the output of the command:

    |               created_on               |       name        | database_name | schema_name |    owner     |           comment            |
------------------------------------------+-------------------+---------------+-------------+--------------+------------------------------
| Fri, 23 Jun 1967 07:00:00.123000 +0000 | TEST_MCP_SERVER   | TEST_DATABASE | TEST_SCHEMA | ACCOUNTADMIN | [NULL]                       |
| Fri, 23 Jun 1967 07:00:00.123000 +0000 | TEST_MCP_SERVER_2 | TEST_DATABASE | TEST_SCHEMA | ACCOUNTADMIN | Test MCP server with comment |

  2. To describe an MCP server, use the following command:

    DESCRIBE MCP SERVER <server_name>;

    The following shows the output of the command:

    |      name       | database_name | schema_name |    owner     | comment |     server_spec        |               created_on               |
------------------------------------------------------------------------------------------------------+-------------------------------------
| TEST_MCP_SERVER | TEST_DATABASE | TEST_SCHEMA | ACCOUNTADMIN | [NULL]  | {"version":1,"tools":[{"name":"product-search","identifier":"db.schema.search_service","type":"CORTEX_SEARCH_SERVICE_QUERY"}]} | Fri, 23 Jun 1967 07:00:00.123000 +0000 |

  3. To drop an MCP server, use the following command:

    DROP MCP SERVER <server_name>;

MCP server URL

To connect to the MCP server, use the URL endpoint with the following format:

https://<account_URl>/api/v2/databases/{database}/schemas/{schema}/mcp-servers/{name}

For information about formatting your account URL, see Account identifiers.

访问控制

You can use the following privileges to manage access to the MCP server and the underlying tools.

PrivilegeObjectDescription
CREATEMCP SERVERRequired to create the MCP server
OWNERSHIPMCP SERVER必须拥有此权限才能更新对象配置
MODIFYMCP SERVERProvides update, drop, describe, show, and use (tools/list and tools/call) on the object configuration
USAGEMCP SERVER必须拥有此权限才能连接 MCP 服务器并发现工具
USAGECortex Search 服务必须拥有此权限才能调用 MCP 服务器中的 Cortex Search 工具
SELECT语义视图必须拥有此权限才能调用 MCP 服务器中的 Cortex Analyst 工具
USAGECortex AgentRequired to invoke the Cortex Agent as a tool in the MCP server
USAGEUser-defined function (UDF) or stored procedureRequired to invoke the UDF or stored procedure as a tool in the MCP server

Set up OAuth authentication

Configure authentication on the MCP client. The Snowflake-managed MCP server supports OAuth 2.0 aligned with the authorization (https://modelcontextprotocol.io/specification/2025-11-25/basic/authorization) recommendation in the MCP protocol. The Snowflake-managed MCP server doesn’t support dynamic client registration.

  1. First, create the security integration. For information about this command, see CREATE SECURITY INTEGRATION (Snowflake OAuth).

    CREATE [ OR REPLACE ] SECURITY INTEGRATION [IF NOT EXISTS] <integration_name>
  TYPE = OAUTH
  OAUTH_CLIENT = CUSTOM
  ENABLED = TRUE
  OAUTH_CLIENT_TYPE = 'CONFIDENTIAL'
  OAUTH_REDIRECT_URI = '<redirect_URI>'

  2. 然后，调用系统函数，以检索客户端配置所需的客户端 ID 和密钥。集成名称区分大小写，并且必须为大写形式。

    SELECT SYSTEM$SHOW_OAUTH_CLIENT_SECRETS('<integration_name>');

Interact with the MCP server using a custom MCP client

For information about building a custom MCP client, see Build an MCP client (https://modelcontextprotocol.io/docs/develop/build-client).

Note

The Snowflake MCP server currently only supports tool capabilities.

发现与调用工具

The MCP clients can discover and invoke tools with tools/list and tools/call requests.

To discover or invoke tools, issue a POST call as shown in the tools/list request (https://modelcontextprotocol.io/specification/2025-11-25/server/tools#calling-tools):

For the Analyst tool, your client passes messages in the request. The SQL statement is listed in the output. You must pass the name of the tool that you’re invoking in the request in the name parameter.

POST /api/v2/databases/<database>/schemas/<schema>/mcp-servers/<name>
    {
        "jsonrpc": "2.0",
        "id": 1,
        "method": "tools/call",
        "params": {
            "name": "test-analyst",
            "arguments": {
                "message": "text"
            }
        }
    }

The following example shows the response:

{
    "jsonrpc": "2.0",
    "id": 1,
    "result": {
        "content": [
            {
                "type": "text",
                "text": "string"
            }
        ]
    }
}

For Search tool requests, your client can pass the query and the following optional arguments:

  • columns
  • limit

The search results and request ID are returned in the output. You must pass the name of the tool that you’re invoking in the request as the name parameter.

POST /api/v2/databases/{database}/schemas/{schema}/mcp-servers/{name}
    {
        "jsonrpc": "2.0",
        "id": 1,
        "method": "tools/call",
        "params": {
            "name": "product-search",
            "arguments": {
                "query": "Hotels in NYC",
                "columns": array of strings,
                "limit": int
            }
        }
  }

The following example shows the response:

{
    "jsonrpc": "2.0",
    "id": 1,
    "result": {
        "results": {}
    }
}

限制

Snowflake 管理的 MCP 在 MCP 协议中不支持以下结构：资源、提示、根、通知、版本协商、生命周期阶段和采样。

仅支持非流式传输响应。