MCP Mastery
Build MCP servers & clients with Python, Streamlit, ChromaDB, LangChain, LangGraph agents, and Ollama integrations
What is MCP Mastery?
MCP Mastery is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to build mcp servers & clients with python, streamlit, chromadb, langchain, langgraph agents, and ollama integrations
Build MCP servers & clients with Python, Streamlit, ChromaDB, LangChain, LangGraph agents, and Ollama integrations
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Build MCP servers & clients with Python, Streamlit, ChromaDB
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx mcp-mastery-with-claude-and-langchainConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use MCP Mastery
MCP Mastery with Claude and LangChain is a comprehensive educational repository covering how to build MCP servers and clients using Python, Streamlit, ChromaDB, LangChain, LangGraph agents, and Ollama local LLMs. It provides hands-on course materials, code examples, and project templates for developers who want to learn the Model Context Protocol in depth alongside modern AI application patterns. The repository accompanies a structured course and is ideal for engineers moving from basic LLM usage to building production-grade agentic systems.
Prerequisites
- Python 3.10 or later installed
- Anthropic API key for Claude integration
- Ollama installed locally if using local LLM examples
- Basic familiarity with Python async programming and REST APIs
- An MCP-compatible client such as Claude Desktop for testing built servers
Clone the repository
Clone the course repository to your local machine.
git clone https://github.com/laxmimerit/MCP-Mastery-with-Claude-and-Langchain.git
cd MCP-Mastery-with-Claude-and-LangchainCreate a virtual environment
Set up a Python virtual environment to isolate course dependencies.
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activateInstall dependencies
Install the required Python packages for the course examples.
pip install -r requirements.txtSet API keys
Export your Anthropic API key and any other required keys as environment variables.
export ANTHROPIC_API_KEY=your_anthropic_api_keyExplore and run individual modules
The repo is organized into topic modules covering MCP servers, LangChain agents, LangGraph workflows, ChromaDB vector stores, and Streamlit UIs. Navigate into the relevant module directory and run the example scripts.
# Example: run a basic MCP server module
python mcp_server_example.pyLaunch Streamlit demos
Several modules include Streamlit-based interactive UIs. Run them to see LangChain and LangGraph agents in action.
streamlit run app.pyMCP Mastery Examples
Client configuration
Example Claude Desktop configuration for connecting to a locally built MCP server from the course materials.
{
"mcpServers": {
"mcp-mastery-local": {
"command": "python",
"args": ["mcp_server_example.py"],
"env": {
"ANTHROPIC_API_KEY": "your_anthropic_api_key"
}
}
}
}Prompts to try
Learning-oriented prompts and tasks to explore after running the course examples.
- "Build a simple MCP server that exposes a calculator tool using Python FastMCP"
- "Create a LangGraph agent that can search a ChromaDB vector store for relevant documents"
- "Show me how to connect an Ollama local LLM to a LangChain chain"
- "Write a Streamlit UI that lets users chat with a LangGraph agent"
- "Explain how MCP tool schemas differ from LangChain tool definitions"Troubleshooting MCP Mastery
Import errors for LangChain or LangGraph modules
LangChain has undergone significant package splits. Ensure you install both langchain and langchain-community, and for LangGraph install langgraph separately: pip install langchain langchain-community langgraph langchain-anthropic
Ollama models not responding or connection refused
Make sure Ollama is running locally with ollama serve and that you have pulled the required model, e.g., ollama pull llama3. The default Ollama base URL is http://localhost:11434.
ChromaDB persistence errors on startup
ChromaDB requires a writable directory for its persistence layer. Set the persist_directory parameter in the Chroma client constructor to an absolute path that your user owns, and ensure the directory exists before running.
Frequently Asked Questions about MCP Mastery
What is MCP Mastery?
MCP Mastery is a Model Context Protocol (MCP) server that build mcp servers & clients with python, streamlit, chromadb, langchain, langgraph agents, and ollama integrations It connects AI assistants to external tools and data sources through a standardized interface.
How do I install MCP Mastery?
Follow the installation instructions on the MCP Mastery GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with MCP Mastery?
MCP Mastery works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is MCP Mastery free to use?
Yes, MCP Mastery is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Set Up MCP Mastery in Your Editor
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Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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