Multi-Capability Proxy Server
A Flask-based server that hosts multiple tools, each exposing functionalities by calling external REST APIs through a unified interface.
README
MCP (Multi-Capability Proxy) Server
This project implements a simple MCP server using Flask. The server can host multiple "tools," where each tool exposes functionalities by calling external REST APIs.
Project Structure
.
├── mcp_server.py # Main Flask application, loads tools and routes requests
├── config.py # Configuration for tools (e.g., API base URLs)
├── requirements.txt # Python dependencies
├── tools/ # Directory for tool implementations
│ ├── __init__.py # Makes 'tools' a Python package
│ └── example_tool.py # An example tool implementation
└── README.md # This file
Setup and Running
-
Clone the repository (if applicable) or ensure all files are in place.
-
Create a virtual environment (recommended):
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate -
Install dependencies:
pip install -r requirements.txt -
Run the server:
python mcp_server.pyThe server will start, by default on
http://127.0.0.1:5001. It will also create thetoolsdirectory and a basicexample_tool.pyif they don't exist upon first run (though they are included in this setup).
Using the MCP Server
The server exposes the following endpoints:
GET /: Lists all available tools and their descriptions.GET /<tool_name>/<action>?param1=value1¶m2=value2: Executes an action for a specific tool using GET. Parameters are passed as query strings.POST /<tool_name>/<action>: Executes an action for a specific tool using POST. Parameters should be sent as a JSON body.
Example: Interacting with example_tool
The example_tool interacts with https://jsonplaceholder.typicode.com.
-
List available tools:
curl http://127.0.0.1:5001/This will show
example_tooland its available actions. -
Get all posts (using
example_tool):curl http://127.0.0.1:5001/example_tool/get_posts -
Get a specific post by ID (using
example_tool):curl http://127.0.0.1:5001/example_tool/get_post_by_id?id=1 -
Create a new post (using
example_tool):curl -X POST -H "Content-Type: application/json" \ -d '{"data": {"title": "My New Post", "body": "This is the content.", "userId": 1}}' \ http://127.0.0.1:5001/example_tool/create_post
Adding New Tools
- Create a new Python file in the
tools/directory (e.g.,my_new_tool.py). - Implement the
execute(action, params)function:- This function will receive the
actionname (string) andparams(dictionary) from the request. - It should contain the logic to call the external API based on the action and params.
- It must return a JSON-serializable dictionary or list.
- This function will receive the
- Implement the
get_tool_description()function:- This function should return a dictionary describing the tool, including its name, a general description, and a dictionary of available actions with their descriptions and parameters. See
tools/example_tool.pyfor a template.
- This function should return a dictionary describing the tool, including its name, a general description, and a dictionary of available actions with their descriptions and parameters. See
- (Optional) Add configuration for your new tool in
config.pyand import it into your tool file. - Restart the MCP server. It will automatically detect and load the new tool.
Example structure for a new tool (tools/my_new_tool.py):
import requests
# from config import MY_NEW_TOOL_CONFIG # If you have config
# MY_API_BASE_URL = MY_NEW_TOOL_CONFIG.get("BASE_URL", "default_url_if_any")
def execute(action, params=None):
if params is None:
params = {}
if action == "some_action":
# api_key = params.get("api_key")
# some_id = params.get("id")
# response = requests.get(f"{MY_API_BASE_URL}/endpoint/{some_id}?key={api_key}")
# response.raise_for_status()
# return response.json()
return {"message": f"Action 'some_action' executed with params: {params}"}
elif action == "another_action":
# data_payload = params.get("data")
# response = requests.post(f"{MY_API_BASE_URL}/other_endpoint", json=data_payload)
# response.raise_for_status()
# return response.json()
return {"message": f"Action 'another_action' executed with data: {params.get('data')}"}
else:
return {"error": f"Action '{action}' not found in my_new_tool"}
def get_tool_description():
return {
"name": "My New Tool",
"description": "This tool does new and exciting things.",
"actions": {
"some_action": {
"description": "Performs some action that requires an ID.",
"params": {"id": "string (required)", "api_key": "string (optional)"},
"method": "GET", # Or POST, PUT, DELETE
"path_template": "/endpoint/{id}"
},
"another_action": {
"description": "Performs another action that requires a data payload.",
"params": {"data": "object (required in body)"},
"method": "POST",
"path_template": "/other_endpoint"
}
}
}
This provides a flexible way to extend the MCP server's capabilities.
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。