Demo
A Python MCP server for Cline integration, providing tools for file analysis and other tasks. Supports switching to Google Gemini API.
README
Project Setup and Usage
This project contains a Python server that can be run locally and integrated with Cline as a remote MCP server.
0. Create .env file
If your project requires environment variables (e.g., API keys, database credentials), create a .env file in the root directory of the project.
Example .env content:
GOOGLE_API_KEY="<your-google-api-key-here>"
COHERE_API_KEY="<your-cohere-api-key-here>"
Note: Do not commit your .env file to version control as it may contain sensitive information.
1. Install Dependencies
Ensure you have Python and pip installed. Then, install the required Python dependencies using the requirements.txt file:
pip install -r requirements.txt
2. Run the Python Server
Start the local server by executing the server.py script:
python server.py
This will start the server, typically on http://localhost:8000. Please ensure it works.
3. Configure Remote Server in Cline
To use the tools provided by this server within Cline, you need to configure it as a remote MCP server:
- Open Cline settings. You can usually find this by clicking on the gear icon or navigating through the settings menu in your IDE (e.g., VS Code).
- Look for "MCP servers".
- Add a new remote server configuration with the following details:
- Server Name:
Demo - Server URL:
http://localhost:8000/sse
- Server Name:
After saving these settings, Cline should be able to connect to your local server and expose its tools.
4. Switching to Google Gemini API
By default, some tools may use other API providers. If you wish to use Google's Gemini models, you will need to perform the following steps:
-
Ensure you have a
GOOGLE_API_KEYset in your.envfile, as described in Step 0. -
Manually edit the tool files. Some tool files (e.g.,
static_tools/file_analysis_tool.py,static_tools/meta_tool.py) contain commented-out code for usingChatGoogleGenerativeAI. You will need to:- Comment out the line that initializes the current LLM (e.g.,
ChatOpenAI). - Uncomment the line that initializes
ChatGoogleGenerativeAI.
Example in
static_tools/file_analysis_tool.py:# Comment out the existing LLM # llm = ChatOpenAI(...) # Uncomment the Google Gemini LLM llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0, google_api_key=GOOGLE_API_KEY) - Comment out the line that initializes the current LLM (e.g.,
-
Restart the server. After making these changes, restart the Python server (
python server.py) for the changes to take effect.
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。