MCP_Python
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP_Pythonhow to deploy Azure Container Apps?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Server — LearnAgent on Azure Functions
MCP (Model Context Protocol) server deployed on Azure Functions that exposes a LearnAgent tool: any MCP-compatible client (Claude Code, VS Code, custom agents) can call it to search and retrieve official Microsoft Learn documentation in real time.
Built with Azure AI Foundry as the LLM backend. Two components:
agent_client.py— local agent that queries Microsoft Learn docs via MCPserver/— MCP server deployed on Azure Functions, exposes aLearnAgenttool
Consuming the deployed MCP server
Endpoint:
https://azure-function-jnb-agents.azurewebsites.net/runtime/webhooks/mcpTransport: 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 tsvAvailable tool
Tool | Input | Description |
|
| 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.txtCreate .env:
FOUNDRY_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
FOUNDRY_MODEL=gpt-4o-minipython agent_client.pyDeploying 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 trueRequired app settings on the Function App:
Setting | Value |
| Azure AI Foundry project endpoint |
| 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 |
|
Agent orchestration |
|
Azure Functions hosting |
|
Azure AI Foundry LLM |
|
Azure auth |
|
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