Demo
Enables use of Google's Gemini models (e.g., gemini-1.5-flash) for tools like file analysis and meta-tools, configured via a GOOGLE_API_KEY environment variable.
Default LLM integration using OpenAI's ChatOpenAI for powering various server tools.
Click on "Install 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., "@Demoanalyze the main.py file"
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.
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.txt2. Run the Python Server
Start the local server by executing the server.py script:
python server.pyThis 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:
DemoServer URL:
http://localhost:8000/sse
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)Restart the server. After making these changes, restart the Python server (
python server.py) for the changes to take effect.
This server cannot be installed
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Latest Blog Posts
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/fgh23333/mcp_server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server