This Streamlit application provides a user interface for connecting to MCP (Model Context Protocol) servers and interacting with them using different LLM providers (OpenAI, Anthropic, Google...).

• Connect to MCP servers via SSE (Server-Sent Events)
• Support for both single server and multiple server configurations
• Select between different LLM providers (OpenAI/Claude)
• View, test, and use available MCP tools directly from the UI
• Chat interface for interacting with the LLM agent
• Tool execution results display

1. Clone this repository:

git clone https://github.com/guinacio/langchain-mcp-client.git
cd langchain-mcp-client

2. Create a virtual environment and install dependencies:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Run the Streamlit app with:

streamlit run app.py

The application will be available at http://localhost:8501

To use this application, you'll need an MCP server running or a valid URL to an MCP server. Use the simple MCP server available on weather_server.py for a quick test:

2. Install the MCP library:

pip install mcp

3. Run the server:

python weather_server.py

The server will start on port 8000 by default. In the Streamlit app, you can connect to it using the URL http://localhost:8000/sse.

• OpenAI: Requires an OpenAI API key and supports models like gpt-4o, gpt-4, and gpt-3.5-turbo
• Anthropic: Requires an Anthropic API key and supports Claude models
• Google: Requires a Google Generative Language / Vertex AI API
• Local LLMs: Supports a local LLM using Ollama

• Currently supports SSE (Server-Sent Events) connections
• Enter the URL of your MCP server's SSE endpoint (e.g., http://localhost:8000/sse)

• Single Server: Connect to a single MCP server
• Multiple Servers: Connect to multiple MCP servers simultaneously
  • Add servers with unique names
  • Manage (add/remove) servers through the UI
  • Connect to all configured servers at once

• View all available tools from connected MCP servers in the sidebar
• Each tool displays:
  • Name and description
  • Required and optional parameters with their types
  • Parameter descriptions and constraints
• Tools are automatically available to the LLM agent in the chat interface
• Tool executions and their results are tracked in the chat history

• STDIO MCP Servers: Support for connecting to MCP servers using standard input/output (STDIO) for more flexible server configurations.
• Test Tools Individually: Implement functionality to test each tool individually from the UI to ensure they work as expected.
• ✅ Using Local LLMs: Support for connecting local LLMs (Llama, DeepSeek, Qwen...)
• Agent Memory: Introduce memory capabilities for the agent to retain context across interactions.
• RAG (File Upload): Enable Retrieval-Augmented Generation (RAG) by allowing users to upload files that the agent can use to enhance its responses.

• Connection Issues: Ensure your MCP server is running and accessible
• API Key Errors: Verify that you've entered the correct API key for your chosen LLM provider
• Tool Errors: Check the server logs for details on any errors that occur when using tools

• LangChain MCP Adapters
• Model Context Protocol
• Streamlit Documentation