mcp-http-deploy
Click on "Deploy 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-http-deployShow me the available tools and test the API connection"
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.
교육용 MCP 서버
파일
mcp_server_http.py 서버 (도구 4개, transport="http")
make_data.py 교육용 더미 데이터 생성 (이미지 빌드 때 실행됨)
Dockerfile python:3.12-slim 기반
docker-compose.yml mcp-server + gateway 2개 서비스
nginx/default.conf.template 인증, 사용량 제한, 호출 기록 설정
client_test.py 접속 확인용 (langchain-mcp-adapters)Related MCP server: Gemini Streamable HTTP MCP
실행
docker compose up -d
docker compose ps
python client_test.py
docker compose downuvicorn
fastmcp의 mcp.run(transport="http")이 내부에서 uvicorn을 띄웁니다.
서버 기동 로그에서 확인할 수 있습니다.
INFO: Uvicorn running on http://0.0.0.0:8000uvicorn은 비동기라 단일 프로세스로도 여러 클라이언트를 동시에 받습니다.
MCP streamable HTTP는 세션 상태를 프로세스 메모리에 둡니다. 트래픽이 정말 늘면 워커가 아니라 컨테이너 개수를 늘리고 로드밸런서를 앞에 둡니다.
Agent에서 연결
mcp_agent.py의 설정에서 transport와 url만 바뀝니다.
# stdio (4일차)
client = MultiServerMCPClient({
"company": {"command": sys.executable, "args": ["mcp_server.py"], "transport": "stdio"},
})
# streamable HTTP (이 서버, 게이트웨이 경유)
client = MultiServerMCPClient({
"company": {
"url": "http://서버주소:8080/mcp",
"transport": "streamable_http",
"headers": {"X-API-Key": "발급받은키"},
},
})나머지 코드(get_tools, create_agent)는 그대로입니다. 도구를 서버로 빼고 규약으로 주고받는다는 MCP의 약속 덕분입니다.
This server cannot be deployed
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