[ MCP SERVER SUMMARY ]
Connect to data, AI tools & agents, and the rest of the Databricks platform using turnkey managed MCP servers. Or, host your own custom MCP servers within the Databricks security and data governance boundary. Connect Databricks through Bifrost MCP Gateway to centralize access control, virtual keys, and tool-call observability for your AI assistants.
| Property | Details |
|---|---|
| Company | Upstream maintainer |
| Connection type | Confirm upstream transport |
| Authentication | Varies by upstream server |
| Package or endpoint | See upstream documentation |
| Capabilities | Databricks tools over MCP, AI assistant connectivity, Governed tool access through Bifrost |
| Official documentation | Databricks MCP documentation |
[ HOW TO CONNECT ]
Start the gateway locally or deploy in your VPC.
npx -y @maximhq/bifrost
Open MCP Gateway in the Bifrost dashboard, click New MCP Server, and select the transport documented by the upstream server. Or register via the API below.
• Confirm the transport, command or endpoint, and authentication requirements in the Databricks upstream documentation.
• Replace every placeholder in the example before sending the request.
Configure Claude Desktop, Cursor, or Claude Code to use your Bifrost endpoint with a virtual key instead of connecting directly to Databricks.
{
"mcpServers": {
"bifrost": {
"url": "http://localhost:8080/mcp",
"headers": {
"Authorization": "Bearer vk_your_virtual_key"
}
}
}
}[ EXAMPLE CONFIG ]
Add this MCP client with a POST to /api/mcp/client. See Connecting to MCP Servers for full options.
# For a remote HTTP or SSE server:
curl -X POST http://localhost:8080/api/mcp/client \
-H "Content-Type: application/json" \
-d '{
"name": "databricks",
"connection_type": "<http|sse>",
"connection_string": "<upstream MCP endpoint>",
"tools_to_execute": ["*"]
}'
# For a local STDIO server:
curl -X POST http://localhost:8080/api/mcp/client \
-H "Content-Type: application/json" \
-d '{
"name": "databricks",
"connection_type": "stdio",
"stdio_config": {
"command": "<upstream command>",
"args": ["<arg>"]
},
"tools_to_execute": ["*"]
}'[ WHY BIFROST ]
Virtual keys, RBAC, and tool allowlists per team or agent.
Attribute MCP tool spend alongside LLM usage in one view.
Log every tool call with latency, server, and virtual key metadata.
Upstream secrets stay in the gateway — not on every laptop.
[ FAQ ]
Connect to data, AI tools & agents, and the rest of the Databricks platform using turnkey managed MCP servers. Or, host your own custom MCP servers within the Databricks security and data governance boundary. The exact tools available depend on the upstream server version and configuration.
Check the upstream documentation to determine whether Databricks uses STDIO, Streamable HTTP, or SSE. Then add it as an MCP client in Bifrost using the corresponding command or endpoint.
No. Bifrost connects to Databricks as an upstream MCP client and adds a governed gateway between that server and your AI assistants.
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