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.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.