name: add-tools description: "Add tools to your agent and grant required permissions in databricks.yml. Use when: (1) Adding MCP servers, Genie spaces, vector search, or UC functions to agent, (2) Permission errors at runtime, (3) User says 'add tool', 'connect to', 'grant permission', (4) Configuring databricks.yml resources."
Add Tools & Grant Permissions
Profile reminder: All
databricksCLI 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:
| File | Resource Type | When to Use |
|---|---|---|
uc-function.yaml |
Unity Catalog function | UC functions |
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 |
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.
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 deployafter modifyingdatabricks.yml
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