Add Tools & Grant Permissions

Profile reminder: All databricks CLI commands must include the profile from .env: databricks <command> --profile <profile>

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:

File	Resource Type	When to Use
`uc-function.yaml`	Unity Catalog function	UC functions via MCP
`uc-connection.yaml`	UC connection	External MCP servers
`vector-search.yaml`	Vector search index	RAG applications
`sql-warehouse.yaml`	SQL warehouse	SQL execution
`serving-endpoint.yaml`	Model serving endpoint	Model inference
`genie-space.yaml`	Genie space	Natural language data
`lakebase.yaml`	Lakebase database	Agent memory storage (provisioned)
`lakebase-autoscaling.md`	Lakebase autoscaling postgres	Agent memory storage (autoscaling)
`experiment.yaml`	MLflow experiment	Tracing (already configured)
`custom-mcp-server.md`	Custom MCP apps	Apps starting with `mcp-*`

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.

value_from Pattern

IMPORTANT: Make sure all value_from references in databricks.yml config.env reference an existing key in the databricks.yml resources list. Some resources need environment variables in your app. Use value_from in databricks.yml config.env to reference resources defined in databricks.yml:

# In databricks.yml, under apps.<app>.config.env:
env:
  - name: MLFLOW_EXPERIMENT_ID
    value_from: "experiment"        # References resources.apps.<app>.resources[name='experiment']
  - name: LAKEBASE_INSTANCE_NAME
    value_from: "database"   # References resources.apps.<app>.resources[name='database']

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

Important Notes