FunctionsMcpPrompts

This project is a Python Azure Function app that exposes MCP (Model Context Protocol) prompts as a remote MCP server. Prompts are reusable prompt templates that MCP clients can discover and invoke, optionally with arguments.

Note: MCP tools are in the FunctionsMcpTool project.

Prompts included

Prompt	Arguments	Description
code_review_checklist	(none)	Returns a structured code review checklist for evaluating code changes.
summarize_content	topic (required), audience (optional)	Generates a summarization prompt tailored to a given topic and audience.
generate_documentation	function_name (optional), style (optional)	Generates API documentation for a function.

Key concepts

• Simple prompts (like code_review_checklist) take no arguments and return static prompt text.
• Parameterized prompts use prompt_arguments to accept arguments from the client.
• Prompts can define arguments as required or optional, and read them from context.arguments.

Prerequisites

• Python version 3.13 or higher
• Azure Functions Core Tools >= 4.8.0
• azure-functions Python package version 2.2.0b2 or greater (pre-release)

Important: This project uses the preview extension bundle (Microsoft.Azure.Functions.ExtensionBundle.Preview) configured in host.json. The azure-functions Python package must also be version 2.2.0b2+ to expose the mcp_prompt_trigger decorator.

Run locally

1. Start Azurite

An Azure Storage Emulator is needed for the function app to run locally:

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

Note: If using the Azurite VS Code extension, run Azurite: Start from the command palette.

2. Install Dependencies

Create and activate a virtual environment, then install dependencies:

python3 -m venv .venv

# macOS/Linux
source .venv/bin/activate    

# Windows
.venv\Scripts\activate  

pip install -r requirements.txt

3. Start the Functions host

func start

The MCP endpoint will be available at http://localhost:7071/runtime/webhooks/mcp.

Using the MCP Server

Connect from VS Code - GitHub Copilot

1. Open .vscode/mcp.json
2. Find the server called local-mcp-function and click Start. The server uses the endpoint: http://localhost:7071/runtime/webhooks/mcp
3. In Copilot chat agent mode, access prompts using the slash command format:
   • /mcp.local-mcp-function.code_review_checklist
   • /mcp.local-mcp-function.summarize_content
   • /mcp.local-mcp-function.generate_documentation

For remote servers, replace the server name accordingly (e.g., /mcp.remote-mcp-function.code_review_checklist).

Connect from MCP Inspector

1. Install and run MCP Inspector:

   npx @modelcontextprotocol/inspector
   

2. Open the URL displayed (e.g., http://0.0.0.0:5173/#resources)
3. Set transport type to Streamable HTTP
4. Set URL to http://0.0.0.0:7071/runtime/webhooks/mcp and Connect
5. List Prompts, select a prompt, and Get Prompt

Deploy to Azure

Step 1: Sign in

az login
azd auth login

Step 2: Create an environment

azd env new <environment-name>

This also becomes the resource group name.

Step 3: Provision and deploy

By default, OAuth-based authentication is enabled using the built-in MCP auth feature with Microsoft Entra as the identity provider.

Configure VS Code as an allowed client application for Microsoft Entra:

azd env set PRE_AUTHORIZED_CLIENT_IDS aebc6443-996d-45c2-90f0-388ff96faa56

Optionally enable VNet isolation:

azd env set VNET_ENABLED true

Deploy the project. When prompted, pick your subscription and an Azure region.

azd up

Step 4: Connect to the remote MCP server

Open .vscode/mcp.json and click Start above remote-mcp-function. You'll be prompted for functionapp-name — find it in your azd command output or the .azure/<env>/.env file. Since authentication is enabled, you'll also be prompted to sign in with Microsoft.

Redeploy and clean up

• Redeploy: azd deploy
• Clean up all resources: azd down

Examining the code

Prompts are defined in function_app.py. Each prompt is a Python function with the @app.mcp_prompt_trigger decorator:

@app.mcp_prompt_trigger(
    arg_name="context",
    prompt_name="summarize_content",
    prompt_arguments=[
        func.PromptArgument("topic", "The topic or content to summarize.", required=True),
        func.PromptArgument("audience", "Target audience (e.g., 'executive', 'developer', 'beginner').", required=False)
    ],
    description="Generates a summarization prompt tailored to a given topic and audience."
)
def summarize_content(context: func.PromptInvocationContext) -> str:
    topic = context.arguments.get("topic", "")
    audience = context.arguments.get("audience")
    # Returns a formatted prompt string

The prompt_arguments parameter defines the arguments that MCP clients see when they list available prompts.