### Start Azurite Storage Emulator (Shell)

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

Starts the Azurite Azure Storage Emulator using Docker. This is required for the sample to save and retrieve snippets from blob storage locally.

```shell
docker run -p 10000:10000 -p 10001:10001 -p 10002:10002 \
    mcr.microsoft.com/azure-storage/azurite
```

--------------------------------

### Install Python Dependencies (Shell)

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

Installs the necessary Python dependencies for the Azure Functions project from the requirements.txt file. It's recommended to use a virtual environment.

```shell
cd src
pip install -r requirements.txt
```

--------------------------------

### VS Code Copilot Agent Prompts (Plaintext)

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

Examples of prompts to interact with the Copilot agent in VS Code to trigger MCP server functionalities like saving and retrieving code snippets.

```plaintext
Say Hello
```

```plaintext
Save this snippet as snippet1
```

```plaintext
Retrieve snippet1 and apply to newFile.py
```

--------------------------------

### Start Azure Functions Host (Shell)

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

Starts the Azure Functions host locally, making the MCP server accessible via a webhook endpoint. The default endpoint is provided.

```shell
func start
```

--------------------------------

### Enable VNet for Deployment

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

This command sets an environment variable to enable Virtual Network (VNet) usage for the deployment. It should be run before executing 'azd up'.

```bash
azd env set VNET_ENABLED true
```

--------------------------------

### Run MCP Inspector (Shell)

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

Installs and runs the MCP Inspector tool using Node Package Manager (npm). This tool is used to interact with and inspect the MCP server.

```shell
npx @modelcontextprotocol/inspector
```

--------------------------------

### Deploy to Azure with azd

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

This command provisions Azure resources and deploys the application code. It utilizes the Azure Developer CLI (azd) and assumes the project is configured for deployment.

```shell
azd up
```

--------------------------------

### Redeploy Application with azd deploy

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

This command deploys the latest code updates to the function app without reprovisioning the Azure resources, useful for iterative development.

```bash
azd deploy
```

--------------------------------

### Get Function App Name and Resource Group

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

These bash commands retrieve the Function App Name and Resource Group from the environment configuration file, which are then used for subsequent Azure CLI operations.

```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
```

--------------------------------

### JSON Azure Functions Host Configuration

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

Specifies the `host.json` configuration for an Azure Functions app that utilizes experimental features, specifically referencing the experimental extension bundle required for MCP functionalities.

```json
"extensionBundle": {
  "id": "Microsoft.Azure.Functions.ExtensionBundle.Experimental",
  "version": "[4.*, 5.0.0)"
}
```

--------------------------------

### Delete Azure Resources with azd down

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

This command is used to clean up and delete all provisioned Azure resources associated with the application, preventing further costs.

```shell
azd down
```

--------------------------------

### Download Blob with Azure CLI

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

This command downloads a specific blob from the 'snippets' container to a local file path. It requires the Azure CLI, the blob name, a local file path, and the connection string for the emulator.

```shell
# Download a specific blob to view its contents
az storage blob download --container-name snippets --name <blob-name> --file <local-file-path> --connection-string "DefaultEndpointsProtocol=http;AccountName=devstoreaccount1;AccountKey=Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==;BlobEndpoint=http://127.0.0.1:10000/devstoreaccount1;"
```

--------------------------------

### Python Azure Function: MCP Get Snippet Tool

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

Implements an Azure Function in Python for the 'get_snippet' MCP tool. This function reads a snippet from Azure Blob Storage using an input binding and returns its content. It requires the AzureWebJobsStorage connection and a specific blob path.

```python
@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
```

--------------------------------

### View Function App Logs

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

This Azure CLI command streams the logs from the specified function app in real-time, allowing you to monitor application activity and troubleshoot issues.

```bash
az webapp log tail --name $FUNCTION_APP_NAME --resource-group $RESOURCE_GROUP
```

--------------------------------

### List Blobs with Azure CLI

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

This command lists all blobs within the 'snippets' container of the local Azure Storage Emulator. It requires the Azure CLI and the connection string for the emulator.

```shell
# List blobs in the snippets container
az storage blob list --container-name snippets --connection-string "DefaultEndpointsProtocol=http;AccountName=devstoreaccount1;AccountKey=Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==;BlobEndpoint=http://127.0.0.1:10000/devstoreaccount1;"
```

--------------------------------

### Configure MCP Server for GitHub Copilot in mcp.json

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

This JSON configuration is specifically for GitHub Copilot within VS Code. It defines an input for the system key and sets up a server named 'my-mcp-server' using SSE, with the key provided via the 'x-functions-key' header in the request.

```json
{
    "inputs": [
        {
            "type": "promptString",
            "id": "functions-mcp-extension-system-key",
            "description": "Azure Functions MCP Extension System Key",
            "password": true
        }
    ],
    "servers": {
        "my-mcp-server": {
            "type": "sse",
            "url": "<funcappname>.azurewebsites.net/runtime/webhooks/mcp/sse",
            "headers": {
                "x-functions-key": "${input:functions-mcp-extension-system-key}"
            }
        }
    }
}
```

--------------------------------

### Python Azure Function: MCP Hello Tool

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

Defines a simple Azure Function using Python that acts as an MCP tool named 'hello'. This function returns a basic greeting message and is exposed via MCP function annotations.

```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!"
```

--------------------------------

### Configure MCP Server Connection in mcp.json

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

This JSON configuration defines inputs for system key and function app name, and sets up two servers: 'remote-mcp-function' using SSE with a dynamic URL and 'x-functions-key' header, and 'local-mcp-function' for local testing.

```json
{
    "inputs": [
        {
            "type": "promptString",
            "id": "functions-mcp-extension-system-key",
            "description": "Azure Functions MCP Extension System Key",
            "password": true
        },
        {
            "type": "promptString",
            "id": "functionapp-name",
            "description": "Azure Functions App Name"
        }
    ],
    "servers": {
        "remote-mcp-function": {
            "type": "sse",
            "url": "https://${input:functionapp-name}.azurewebsites.net/runtime/webhooks/mcp/sse",
            "headers": {
                "x-functions-key": "${input:functions-mcp-extension-system-key}"
            }
        },
        "local-mcp-function": {
            "type": "sse",
            "url": "http://0.0.0.0:7071/runtime/webhooks/mcp/sse"
        }
    }
}
```

--------------------------------

### Connect to Remote MCP Server (MCP Inspector)

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

This is a URL format for connecting to the remote MCP server function app from a client, specifically within MCP Inspector. It includes the system key as a query parameter.

```plaintext
https://<funcappname>.azurewebsites.net/runtime/webhooks/mcp/sse?code=<your-mcp-extension-system-key>
```

--------------------------------

### MCP Inspector SSE Endpoint Configuration (Plaintext)

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

Configures the MCP Inspector to use the Server-Sent Events (SSE) transport type and connects to the specified local MCP server endpoint.

```plaintext
http://0.0.0.0:7071/runtime/webhooks/mcp/sse
```

--------------------------------

### Connect to Local MCP Server via SSE (Plaintext)

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

Specifies the Server-Sent Events (SSE) endpoint URL for connecting to the locally running Azure Functions MCP server. This URL is used by clients like VS Code Copilot and MCP Inspector.

```plaintext
http://0.0.0.0:7071/runtime/webhooks/mcp/sse
```

--------------------------------

### Python Azure Function: MCP Save Snippet Tool

Source: https://github.com/azure-samples/remote-mcp-functions-python/blob/main/README.md

Provides a Python Azure Function for the 'save_snippet' MCP tool. This function saves snippet content to Azure Blob Storage using an output binding. It parses snippet name and content from the context and requires the AzureWebJobsStorage connection and a specific blob path.

```python
@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"
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.