Add Tools & Grant Permissions

After adding any MCP server to your agent, you MUST grant the app access in databricks.yml.

Without this, you’ll get permission errors when the agent tries to use the resource.

Workflow

Step 1: Add MCP server in agent_server/agent.py:

from databricks_langchain import DatabricksMCPServer, DatabricksMultiServerMCPClient

genie_server = DatabricksMCPServer(
    url=f"{host}/api/2.0/mcp/genie/01234567-89ab-cdef",
    name="my genie space",
)

mcp_client = DatabricksMultiServerMCPClient([genie_server])
tools = await mcp_client.get_tools()

Step 2: Grant access in databricks.yml:

resources:
  apps:
    agent_langgraph:
      resources:
        - name: 'my_genie_space'
          genie_space:
            name: 'My Genie Space'
            space_id: '01234567-89ab-cdef'
            permission: 'CAN_RUN'

Step 3: Deploy and run:

databricks bundle deploy
databricks bundle run agent_langgraph  # Required to start app with new code!

See deploy skill for more details.

Resource Type Examples

See the examples/ directory for complete YAML snippets:

Custom MCP Servers (Databricks Apps)

Apps are not yet supported as resource dependencies in databricks.yml. Manual permission grant required:

Step 1: Get your agent app’s service principal:

databricks apps get <your-agent-app-name> --output json | jq -r '.service_principal_name'

Step 2: Grant permission on the MCP server app:

databricks apps update-permissions <mcp-server-app-name> \
  --json '{"access_control_list": [{"service_principal_name": "<agent-app-service-principal>", "permission_level": "CAN_USE"}]}'

See examples/custom-mcp-server.md for detailed steps.

valueFrom Pattern (for app.yaml)

IMPORTANT: Make sure all valueFrom references in app.yaml reference an existing key in the databricks.yml file. Some resources need environment variables in your app. Use valueFrom in app.yaml to reference resources defined in databricks.yml:

# app.yaml
env:
  - name: MLFLOW_EXPERIMENT_ID
    valueFrom: "experiment"        # References resources.apps.<app>.resources[name='experiment']
  - name: LAKEBASE_INSTANCE_NAME
    valueFrom: "database"   # References resources.apps.<app>.resources[name='database']

Critical: Every valueFrom value must match a name field in databricks.yml resources.

Important Notes

• MLflow experiment: Already configured in template, no action needed
• Multiple resources: Add multiple entries under resources: list
• Permission types vary: Each resource type has specific permission values
• Deploy + Run after changes: Run both databricks bundle deploy AND databricks bundle run agent_langgraph
• valueFrom matching: Ensure app.yaml valueFrom values match databricks.yml resource name values