HoPe Chatbot MCP Server
Enables modular MCP capabilities by organizing tools, resources, and prompts into composable sub-servers, exposed either all-in-one or as separate MCP endpoints.
README
HoPe Chatbot MCP Server
Uses FastMCP v3.
For code organization reasons MCP functionality is split into logical modules, MCP sub-servers.
Main MCP server can import/mount sub-domain MCP servers. (mcp_server/app.py)
Development
Define your capabilities separated to composable MCP servers.
Define new MCP tools, resources, prompts...
for <some> domain
for <some> business capability
in <some> MCP sub-server
in mcp_server/servers/<some>/mcp.py.
Expose MCP server capabilities
Decide how you want your MCP tools, resources, prompts... to be available:
- in 1 combined MCP server (all-in-one) 🌐
- as separate MCP server only 🔒🪪
- both ✨
All-in-one MCP server
Include sub-domain MCP server in combined MCP server.
Follow these steps in mcp_server/app.py:
-
Import your FastMCP app
from mcp_server.servers.some import some_mcp -
Find main MCP server defined. Add capabilities of your MCP server into main MCP server in startup/setup lifespan phase of main MCP server
async def setup(app: FastMCP): # Import or mount subservers app.mount(some_mcp) # dynamic mount # await app.import_server(some_mcp) # static copy @contextlib.asynccontextmanager async def lifespan(app: FastMCP): # Setup await setup(app) yield # Cleanup main_mcp = FastMCP( name="home-of-performance-mcp-server", lifespan=lifespan ) -
Main combined MCP server contains capabilities of your MCP server at:
BASE_URL/mcp.It should be true for main combined MCP server ran in all cases:
mcp_server/app.pyran as a module- FastMCP.run method
mcp_server/app.pyran with uvicornFastMCP.http_app- ASGI app of your MCP server mount to FastAPI app, uvicorn accessing FastAPI ASGI app
- (uvicorn accessing your ASGI directly will work too)
Separate MCP server
Expose separate sub-domain MCP server.
Follow these steps in mcp_server/app.py:
-
Import your FastMCP app
from mcp_server.servers.some import some_mcp -
Create ASGI app from your FastMCP app
(no need to redefine
path="/mcp"kwarg)some_mcp_app = some_mcp.http_app() -
Add lifespan of your FastMCP app to combined lifespan used by FastAPI app
@contextlib.asynccontextmanager async def combined_lifespan(app: FastAPI): """Combine lifespans for FastAPI app""" # Run all lifespans async with main_mcp_app.lifespan(app): async with some_mcp_app.lifespan(app): # Setup yield # Cleanup fastapi_app = FastAPI( title="Home of Performance MCP", lifespan=combined_lifespan ) -
Mount ASGI app of your MCP server to FastAPI app under
/somepathfastapi_app.mount("/some", some_mcp_app) -
find your separate MCP server at:
BASE_URL/some/mcp
Running locally
-
Define environment variables in
.envaccording to.env.template(do not commit secrets) -
Install required python packages from
requirements.txt -
Start FastMCP app through
runmethodpython -m mcp_server.appor run FastAPI app with MCP server(s) mount into it through gunicorn, uvicorn...
source .venv/bin/activate && uvicorn src.app:app --host 0.0.0.0 --port 8000 --reload
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。