MCP 101 - Build your First MCP App with Langchain

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AI Summary

Summary of the Video Transcript

• Introduction to mCP (Model Context Protocol)
  • Marlene introduces mCP, which has been gaining attention in AI communities.
  • mCP is an open protocol that standardizes how applications provide context to language learning models (LLMs).
  • It is likened to a USB port for AI applications, allowing AI models to connect to different data sources and tools.
  • Marlene is inspired by a demo from Harrison, the founder of Lang chain, to try out the new Lang chain mCP adapters package.
• Getting Started with mCP and Lang chain
  • Marlene directs viewers to the mCP GitHub repository to find pre-built servers for projects.
  • She uses a GitHub server from the repository to build an AI application.
  • The process involves cloning the repository and navigating to the ‘servers’ directory.
• Setting Up the Development Environment
  • Marlene mentions using the Lang chain adapter package from GitHub.
  • She creates a new Python file (Lang chain mCP.py) and copies code from Lang chain into it.
  • A requirements.txt file is used to install necessary packages, including mCP, Lang chain mCP adapter, Lang chain AI, and Flask.
  • Marlene adds Lang chain Azure AI’s Azure chat completions model to the code.
  • Docker is required to run the application locally, and Marlene ensures Docker is installed and running.
• Configuring the Server and Client
  • Marlene sets up the server using Docker and provides a command to build the Docker image.
  • She configures the server to access GitHub repositories using a GitHub personal access token, which can be restricted for safety.
  • The client code in Lang chain is modified to ask a specific question and is wrapped in an asynchronous function (run GitHub agent).
• Running the Application
  • Marlene runs the application, ensuring the server is correctly responding.
  • She adjusts the code to format the response properly and confirms that the output matches the expected results.
• Building a Frontend
  • A Flask server is set up for the backend, and a TypeScript frontend is started using npm start.
  • Marlene demonstrates asking a question through the frontend and verifies the accuracy of the response by comparing it with actual GitHub PRs.
• Conclusion
  • Marlene concludes the tutorial, expressing satisfaction with the setup and results.
  • She encourages viewers to check out the provided links for more information and to share the video if they found it useful.

Detailed Instructions and URLs

• Clone the mCP GitHub repository: git clone [repository URL]
• Navigate to the ‘servers’ directory: cd servers
• Install requirements: pip install -r requirements.txt
• Build the Docker image: [Docker build command from the README]
• Run the Docker server: [Docker run command with local path and permissions]
• Generate a GitHub personal access token: Instructions provided in the video description (exact URL not given).

Notes

• The exact URLs for cloning the repository, Docker commands, and generating a GitHub personal access token are not included in the summary as they were not provided in the transcript text.
• The video focuses on setting up and using mCP with Lang chain, including backend and frontend development.
• Marlene emphasizes the ease of use by copying code from the Lang chain adapter README and making minimal changes.