add-tools

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_openai.agents import McpServer

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

agent = Agent(
    name="my agent",
    model="databricks-claude-3-7-sonnet",
    mcp_servers=[genie_server],
)

Step 2: Grant access in databricks.yml:

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

Step 3: Deploy with databricks bundle deploy (see deploy skill)

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.

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 after changes: Run databricks bundle deploy after modifying databricks.yml