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Run npx skillmds@latest add ramvegiraju/add-tools-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Add Tools free to use?

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Who published Add Tools?

RamVegiraju (@ramvegiraju) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Add Tools by ramvegiraju — Agent Skill

Name: Add Tools
Rating: 4.5 (4 reviews)
Author: RamVegiraju

Add Tools

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.

RamVegiraju Updated Sep 22, 2026

File contents

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:

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
`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.

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

---name: add-tools-2description: 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**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`:```pythonfrom databricks_langchain import DatabricksMCPServer, DatabricksMultiServerMCPClientgenie_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`:```yamlresources: 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:```bashdatabricks bundle deploydatabricks bundle run agent_langgraph # Required to start app with new code!```See **deploy** skill for more details.## Resource Type ExamplesSee 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 || `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:```bashdatabricks apps get <your-agent-app-name> --output json | jq -r '.service_principal_name'```**Step 2:** Grant permission on the MCP server app:```bashdatabricks 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`:```yaml# app.yamlenv: - 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

Frequently asked questions