Content
<!--
---
name: Remote MCP with Azure Functions (Python)
description: Run a remote MCP server on Azure functions.
page_type: sample
languages:
- python
- bicep
- azdeveloper
products:
- azure-functions
- azure
- entra-id
- azure-openai
- ai-foundry
urlFragment: foundry-agent-service-remote-mcp-python
---
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# Getting Started with Agent Service and Remote MCP Servers
This repository contains implementations for an [Azure AI Foundry Agent Service](https://learn.microsoft.com/en-us/azure/ai-foundry/agents/) client with a custom remote MCP server using [Azure Functions Remote MCP](https://learn.microsoft.com/en-us/azure/azure-functions/functions-bindings-mcp). You can clone/restore/run on your local machine with debugging, and `azd up` to have it in the cloud in a couple minutes. The MCP server is secured by design using keys and HTTPS, and allows more options for OAuth using built-in auth and/or [API Management](https://aka.ms/mcp-remote-apim-auth) as well as network isolation using VNET.
## Language Implementations
This repository contains implementations in multiple languages:
- **Python** (Original): `src/agent/` and `src/mcp_server/`
- **.NET/C#** (Ported): `src/Agent/` and `src/McpServer/`
If you're looking for this sample in more languages check out the [.NET/C#](https://github.com/Azure-Samples/remote-mcp-functions-dotnet) and [Node.js/TypeScript](https://github.com/Azure-Samples/remote-mcp-functions-typescript) versions.
[](https://codespaces.new/Azure-Samples/remote-mcp-functions-python)
Below is the architecture diagram for the Remote MCP Server using Azure Functions with Foundry Agent Service:
## Prerequisites
### Python Implementation
+ [Python](https://www.python.org/downloads/) version 3.11 or higher
+ [Azure Functions Core Tools](https://learn.microsoft.com/azure/azure-functions/functions-run-local?pivots=programming-language-python#install-the-azure-functions-core-tools) >= `4.0.7030`
+ [Azure Developer CLI](https://aka.ms/azd)
+ To use Visual Studio Code to run and debug locally:
+ [Visual Studio Code](https://code.visualstudio.com/)
+ [Azure Functions extension](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-azurefunctions)
### .NET Implementation
+ [.NET 8.0 SDK](https://dotnet.microsoft.com/download/dotnet/8.0) or higher
+ [Azure Functions Core Tools](https://learn.microsoft.com/azure/azure-functions/functions-run-local#install-the-azure-functions-core-tools) >= `4.0.7030`
+ [Azure Developer CLI](https://aka.ms/azd)
+ To use Visual Studio Code to run and debug locally:
+ [Visual Studio Code](https://code.visualstudio.com/)
+ [C# extension](https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.csharp)
+ [Azure Functions extension](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-azurefunctions)
## Deploy Remote MCP Server to Azure
Run this [azd](https://aka.ms/azd) command to provision the function app, with any required Azure resources including AI Foundry Agent Service, and deploy your code:
```shell
azd up
```
> **Note**: You'll be prompted to specify an `agentLocation` during deployment. This must be one of the AI Foundry supported regions: `westus`, `westus2`, `uaenorth`, `southindia`, or `switzerlandnorth`. This location is used specifically for AI resources (AI Services, Search, Cosmos DB) and can be different from your main deployment location.
Additionally, [API Management]() can be used for improved security and policies over your MCP Server, and [App Service built-in authentication](https://learn.microsoft.com/azure/app-service/overview-authentication-authorization) can be used to set up your favorite OAuth provider including Entra.
## Connect to your *remote* MCP server function app from a client
Your client will need a key in order to invoke the new hosted SSE endpoint, which will be of the form `https://<funcappname>.azurewebsites.net/runtime/webhooks/mcp/sse`. The hosted function requires a system key by default which can be obtained from the [portal](https://learn.microsoft.com/azure/azure-functions/function-keys-how-to?tabs=azure-portal) or the CLI (`az functionapp keys list --resource-group <resource_group> --name <function_app_name>`). Obtain the system key named `mcp_extension`.
### Foundry Agent Service Client
1. Change to the agent folder in a new terminal window:
```shell
cd agent
```
2. Create a `.env` file based on the example provided. Copy the `.env.example` file:
```shell
copy .env.example .env
```
3. Edit the `.env` file with your deployed function app details:
```env
# Azure AI Project Configuration
PROJECT_ENDPOINT=https://your-agent-service-resource.services.ai.azure.com/api/projects/your-project-name
MODEL_DEPLOYMENT_NAME=gpt-4.1-mini
MCP_SERVER_LABEL=Azure_Functions_MCP_Server
MCP_SERVER_URL=https://<your-funcappname>.azurewebsites.net/runtime/webhooks/mcp/sse
USER_MESSAGE=Create a snippet called snippet1 that prints 'Hello, World!' in Python.
# Required: Azure Functions extension key for MCP server authentication
MCP_EXTENSION_KEY=your_mcp_extension_system_key_here
```
> **Note**: Replace the following values with outputs from your `azd up` deployment:
> - `PROJECT_ENDPOINT`: Your Azure AI Project endpoint (from azd deployment output)
> - `<your-funcappname>`: Your function app name (from azd deployment output)
> - `your_mcp_extension_system_key_here`: The `mcp_extension` system key obtained from the Azure portal or CLI
4. Install Python dependencies for the agent:
```shell
pip install -r requirements.txt
```
5. Run the agent service:
```shell
python main.py
```
The agent will connect to your remote MCP server and execute the message specified in the `USER_MESSAGE` environment variable, demonstrating the integration between Azure AI Foundry and your deployed MCP server.
### .NET Agent Service Client
1. Change to the .NET agent folder in a new terminal window:
```shell
cd src/Agent
```
2. Create a `.env` file based on the example provided. Copy the `.env.example` file:
```shell
cp .env.example .env
```
3. Edit the `.env` file with your deployed function app details (same format as Python):
```env
# Azure AI Project Configuration
PROJECT_ENDPOINT=https://your-agent-service-resource.services.ai.azure.com/api/projects/your-project-name
MODEL_DEPLOYMENT_NAME=gpt-4.1-mini
MCP_SERVER_LABEL=Azure_Functions_MCP_Server
MCP_SERVER_URL=https://<your-funcappname>.azurewebsites.net/runtime/webhooks/mcp/sse
USER_MESSAGE=Create a snippet called snippet1 that prints 'Hello, World!' in Python.
# Required: Azure Functions extension key for MCP server authentication
MCP_EXTENSION_KEY=your_mcp_extension_system_key_here
```
4. Restore .NET dependencies for the agent:
```shell
dotnet restore
```
5. Run the .NET agent service:
```shell
dotnet run
```
The .NET implementation provides a complete Azure AI Foundry agent workflow using the Azure.AI.Agents.Persistent v1.1.0-beta.3 package, including:
- ✅ **Complete agent lifecycle**: Agent creation, thread management, and run execution
- ✅ **Tool call handling**: Supports function tools and MCP tool integration structure
- ✅ **Environment validation**: Configuration validation and secure credential handling
- ✅ **Comprehensive logging**: Detailed workflow monitoring and error handling
- ✅ **Resource cleanup**: Proper resource management and cleanup
- 🔄 **MCP integration**: Structure ready for MCP tool configuration
**Current Status**: The implementation demonstrates the complete agent workflow. MCP tools will be integrated using the exact pattern from the Python implementation once the specific MCP tool definition class is identified in the Azure.AI.Agents.Persistent package.
### Connect to remote MCP server in MCP Inspector
For MCP Inspector, you can include the key in the URL:
```plaintext
https://<funcappname>.azurewebsites.net/runtime/webhooks/mcp/sse?code=<your-mcp-extension-system-key>
```
## Redeploy your code
You can run the `azd up` command as many times as you need to both provision your Azure resources and deploy code updates to your function app.
>[!NOTE]
>Deployed code files are always overwritten by the latest deployment package.
## Clean up resources
When you're done working with your function app and related resources, you can use this command to delete the function app and its related resources from Azure and avoid incurring any further costs:
```shell
azd down
```
## Helpful Azure Commands
Once your application is deployed, you can use these commands to manage and monitor your application:
```bash
# Get your function app name from the environment file
FUNCTION_APP_NAME=$(cat .azure/$(cat .azure/config.json | jq -r '.defaultEnvironment')/env.json | jq -r '.FUNCTION_APP_NAME')
echo $FUNCTION_APP_NAME
# Get resource group
RESOURCE_GROUP=$(cat .azure/$(cat .azure/config.json | jq -r '.defaultEnvironment')/env.json | jq -r '.AZURE_RESOURCE_GROUP')
echo $RESOURCE_GROUP
# View function app logs
az webapp log tail --name $FUNCTION_APP_NAME --resource-group $RESOURCE_GROUP
# Redeploy the application without provisioning new resources
azd deploy
```
## Debugging MCP server function locally
An Azure Storage Emulator is needed for this particular sample because we will save and get snippets from blob storage.
1. Start Azurite
```shell
docker run -p 10000:10000 -p 10001:10001 -p 10002:10002 \
mcr.microsoft.com/azure-storage/azurite
```
>**Note** if you use Azurite coming from VS Code extension you need to run `Azurite: Start` now or you will see errors.
## Run your MCP Server locally from the terminal
### Python MCP Server
1. Change to the src/mcp_server folder in a new terminal window:
```shell
cd src/mcp_server
```
2. Install Python dependencies:
```shell
pip install -r requirements.txt
```
>**Note** it is a best practice to create a Virtual Environment before doing the `pip install` to avoid dependency issues/collisions, or if you are running in CodeSpaces. See [Python Environments in VS Code](https://code.visualstudio.com/docs/python/environments#_creating-environments) for more information.
3. Start the Functions host locally:
```shell
func start
```
### .NET MCP Server
1. Change to the src/McpServer folder in a new terminal window:
```shell
cd src/McpServer
```
2. Restore .NET dependencies:
```shell
dotnet restore
```
3. Start the Functions host locally:
```shell
func start
```
> **Note** by default this will use the webhooks route: `/runtime/webhooks/mcp/sse`. Later we will use this in Azure to set the key on client/host calls: `/runtime/webhooks/mcp/sse?code=<system_key>`
## Connect to the *local* MCP server from a client/host
### Foundry Agent Service Client
The Foundry Agent Service is a cloud service that expects MCP tools that are also in the cloud (e.g. same VNET or on public internet). Proceed to the steps around deploying the Azure for Remote MCP.
### MCP Inspector
1. In a **new terminal window**, install and run MCP Inspector
```shell
npx @modelcontextprotocol/inspector
```
2. CTRL click to load the MCP Inspector web app from the URL displayed by the app (e.g. http://0.0.0.0:5173/#resources)
3. Set the transport type to `SSE`
4. Set the URL to your running Function app's SSE endpoint and **Connect**:
```shell
http://0.0.0.0:7071/runtime/webhooks/mcp/sse
```
>**Note** this step will not work in CodeSpaces. Please move on to Deploy to Remote MCP.
## Source Code
The function code for the `get_snippet` and `save_snippet` endpoints are defined in the Python files in the `src/mcp_server` directory. The MCP function annotations expose these functions as MCP Server tools.
Here's the actual code from the function_app.py file:
```python
@app.generic_trigger(arg_name="context", type="mcpToolTrigger", toolName="hello",
description="Hello world.",
toolProperties="[]")
def hello_mcp(context) -> None:
"""
A simple function that returns a greeting message.
Args:
context: The trigger context (not used in this function).
Returns:
str: A greeting message.
"""
return "Hello I am MCPTool!"
@app.generic_trigger(
arg_name="context",
type="mcpToolTrigger",
toolName="getsnippet",
description="Retrieve a snippet by name.",
toolProperties=tool_properties_get_snippets_json
)
@app.generic_input_binding(
arg_name="file",
type="blob",
connection="AzureWebJobsStorage",
path=_BLOB_PATH
)
def get_snippet(file: func.InputStream, context) -> str:
"""
Retrieves a snippet by name from Azure Blob Storage.
Args:
file (func.InputStream): The input binding to read the snippet from Azure Blob Storage.
context: The trigger context containing the input arguments.
Returns:
str: The content of the snippet or an error message.
"""
snippet_content = file.read().decode("utf-8")
logging.info(f"Retrieved snippet: {snippet_content}")
return snippet_content
@app.generic_trigger(
arg_name="context",
type="mcpToolTrigger",
toolName="savesnippet",
description="Save a snippet with a name.",
toolProperties=tool_properties_save_snippets_json
)
@app.generic_output_binding(
arg_name="file",
type="blob",
connection="AzureWebJobsStorage",
path=_BLOB_PATH
)
def save_snippet(file: func.Out[str], context) -> str:
content = json.loads(context)
snippet_name_from_args = content["arguments"][_SNIPPET_NAME_PROPERTY_NAME]
snippet_content_from_args = content["arguments"][_SNIPPET_PROPERTY_NAME]
if not snippet_name_from_args:
return "No snippet name provided"
if not snippet_content_from_args:
return "No snippet content provided"
file.set(snippet_content_from_args)
logging.info(f"Saved snippet: {snippet_content_from_args}")
return f"Snippet '{snippet_content_from_args}' saved successfully"
```
Note that the `host.json` file also includes a reference to the experimental bundle, which is required for apps using this feature:
```json
"extensionBundle": {
"id": "Microsoft.Azure.Functions.ExtensionBundle.Experimental",
"version": "[4.*, 5.0.0)"
}
```
## Next Steps
- Add [API Management](https://aka.ms/mcp-remote-apim-auth) to your MCP server (auth, gateway, policies, more!)
- Add [built-in auth](https://learn.microsoft.com/en-us/azure/app-service/overview-authentication-authorization) to your MCP server
- Enable VNET using VNET_ENABLED=true flag
- Learn more about [related MCP efforts from Microsoft](https://github.com/microsoft/mcp/tree/main/Resources)
