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For Developer

Follow the instructions below to integrate Postgres MCP server to your AI application using our API or SDK.

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Prerequisites

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1. Create a Postgres MCP Server

Use the following endpoint to create a new remote Postgres MCP server instance:

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Request

from klavis import Klavis
from klavis.types import McpServerName

klavis_client = Klavis(api_key="<YOUR_API_KEY>")

# Create a Postgres MCP server instance
postgres_server = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.POSTGRES,
    user_id="<YOUR_USER_ID>",
    platform_name="<YOUR_PLATFORM_NAME>",
)

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Response

{
  "serverUrl": "https://postgres-mcp-server.klavis.ai/sse?instance_id=<instance-id>",
  "instanceId": "<instance-id>"
}
serverUrl specifies the endpoint of the Postgres MCP server, which allows you to interact with PostgreSQL databases.
instanceId is used for authentication and identification of your server instance.

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2. Configure Postgres Connection

To connect to your PostgreSQL database, you need to configure the MCP server with your database credentials. They are usually in the form of postgresql://<user>:<password>@<host>:<port>/<database_name>.

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Setting up Postgres Connection

curl --request POST \
  --url https://api.klavis.ai/mcp-server/instance/set-auth-token \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "instanceId": "<string>",
  "authToken": "<string>"
}'

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Response

{
  "success": true,
  "message": "<string>"
}

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Explore MCP Server Tools

For more details about tool input schema, use the list_tool API.