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MCP_Python

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Python
Remote

MCP_Python

Two components:

  1. agent_client.py — local agent that queries Microsoft Learn docs via MCP

  2. server/ — MCP server deployed on Azure Functions, exposes a LearnAgent tool

Consuming the deployed MCP server

Endpoint:

https://azure-function-jnb-agents.azurewebsites.net/runtime/webhooks/mcp

Transport: Streamable HTTP (MCP protocol 2025-03-26)

Auth: Azure Functions system key in header:

x-functions-key: <mcp_extension_key>

Get the key:

az functionapp keys list \
  --name azure-function-jnb-agents \
  --resource-group NovilloBenitoJaime \
  --query systemKeys.mcp_extension -o tsv

Available tool

Tool

Input

Description

LearnAgent

query (string, required)

Searches and retrieves official Microsoft Learn documentation

Example — Python client

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
import asyncio

KEY = "<mcp_extension_key>"
URL = "https://azure-function-jnb-agents.azurewebsites.net/runtime/webhooks/mcp"

async def main():
    async with streamablehttp_client(URL, headers={"x-functions-key": KEY}) as (r, w, _):
        async with ClientSession(r, w) as session:
            await session.initialize()
            result = await session.call_tool("LearnAgent", {"query": "How to deploy Azure Container Apps?"})
            print(result.content[0].text)

asyncio.run(main())

Install: pip install mcp

Example — VS Code / Claude Code (mcp.json)

{
  "mcpServers": {
    "LearnAgent": {
      "url": "https://azure-function-jnb-agents.azurewebsites.net/runtime/webhooks/mcp",
      "headers": {
        "x-functions-key": "<mcp_extension_key>"
      }
    }
  }
}

Example — curl (raw JSON-RPC)

KEY="<mcp_extension_key>"

# Initialize
curl -X POST https://azure-function-jnb-agents.azurewebsites.net/runtime/webhooks/mcp \
  -H "Content-Type: application/json" \
  -H "x-functions-key: $KEY" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'

# Call tool
curl -X POST https://azure-function-jnb-agents.azurewebsites.net/runtime/webhooks/mcp \
  -H "Content-Type: application/json" \
  -H "x-functions-key: $KEY" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"LearnAgent","arguments":{"query":"Azure Blob Storage Python quickstart"}}}'

Related MCP server: MCP Server on Cloudflare Workers & Azure Functions

Local agent (agent_client.py)

Connects to https://learn.microsoft.com/api/mcp directly and uses Azure AI Foundry as LLM.

Requirements: Python 3.9+, az login

python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt

Create .env:

FOUNDRY_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
FOUNDRY_MODEL=gpt-4o-mini
python agent_client.py

Deploying the server

cd server
# Edit requirements.txt if needed
Compress-Archive -Path function_app.py,host.json,requirements.txt -DestinationPath deploy.zip -Force
az functionapp deployment source config-zip \
  --name azure-function-jnb-agents \
  --resource-group NovilloBenitoJaime \
  --src deploy.zip --build-remote true

Required app settings on the Function App:

Setting

Value

FOUNDRY_PROJECT_ENDPOINT

Azure AI Foundry project endpoint

FOUNDRY_MODEL

Model deployment name

The Function App needs System Assigned Managed Identity with Azure AI Developer role on the Foundry resource.

Stack

Component

Package

MCP transport

mcp>=1.9.0

Agent orchestration

agent-framework>=1.8.1

Azure Functions hosting

agent-framework-azurefunctions==1.0.0b260521

Azure AI Foundry LLM

agent-framework-foundry

Azure auth

azure-identity

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Maintenance

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