Add Tools & Grant Permissions

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

Don't have the resource yet? See create-tools skill first.

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)

Declare the target app as an app resource in databricks.yml — the bundle grants CAN_USE on deploy. Requires Databricks CLI v0.298.0+.

resources:
  apps:
    agent_langgraph:
      resources:
        - name: 'mcp_server'
          app:
            name: 'mcp-my-server'
            permission: CAN_USE

See examples/custom-mcp-server.md for the full flow (agent code + YAML + deploy).

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.

MCP Error Handling

MCP tool calls can fail (network issues, permission errors, timeouts). Use handle_tool_error on MCP servers to catch errors and return them to the LLM instead of crashing the agent:

DatabricksMCPServer(
    name="genie",
    url=f"{host}/api/2.0/mcp/genie/{space_id}",
    handle_tool_error=True,   # Return error messages to LLM instead of raising
    timeout=60.0,             # Increase timeout for slow tools like Genie
)

For local function tools defined with @tool, see create-tools skill > examples/local-python-tools.md for the ToolException + handle_tool_error pattern.

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 {{BUNDLE_NAME}}
• value_from matching: Ensure config.env value_from values match databricks.yml resource name values