MCP Demo - TypeScript Implementation
This is a TypeScript implementation of the MCP: Build Rich-Context AI Apps with Anthropic course from DeepLearning.AI.
Overview
This project demonstrates the Model Context Protocol (MCP) implementation with streamable HTTP capabilities. MCP is an open protocol that standardizes how LLM applications can access context through tools and data resources using a client-server architecture.
⚠️ This project is for educational and demo purposes only.
Features
- MCP client-server architecture implementation
- Streamable HTTP communication
- arXiv paper search functionality
- Paper information extraction
- Tool selection and argument extraction
- Prompt template management
Prerequisites
- Node.js (v16 or higher)
- Yarn package manager
- Anthropic API key
Setup
-
Clone the repository
git clone <repository-url> cd mcp-demo -
Install dependencies
yarn install -
Environment Configuration
Create a
.envfile in the root directory:ANTHROPIC_API_KEY=<your_anthropic_api_key_here>Important: Replace
<your_anthropic_api_key_here>with your actual Anthropic API key. -
Build the project
yarn build
Project Structure
mcp-demo/
├── src/
│ ├── client.ts # MCP client implementation
│ ├── server.ts # MCP server implementation
│ └── index.ts # Core functionality and utilities
├── package.json # Dependencies and scripts
├── tsconfig.json # TypeScript configuration
├── yarn.lock # Locked dependencies
└── README.md # This file
Usage
Starting the MCP Server
yarn start:server
Starting the MCP Client
yarn start:client
Running Both (Development)
yarn dev
Available Tools
The MCP server provides the following tools:
-
search_papers- Search for papers on arXiv- Arguments:
topic(string): The topic to search formax_results(number, optional): Maximum number of results (default: 5)
- Arguments:
-
extract_info- Extract information from a specific paper- Arguments:
paper_id(string): The ID of the paper to look for
- Arguments:
API Reference
search_papers(topic: string, max_results?: number)
Searches for papers on arXiv based on a topic and returns their information.
extract_info(paper_id: string)
Searches for information about a specific paper by ID from arXiv.
getToolSelectionPrompt(toolList: string, userQuery: string)
Generates a detailed prompt for tool selection and argument extraction.
Course Reference
This implementation is based on the MCP: Build Rich-Context AI Apps with Anthropic course by DeepLearning.AI in partnership with Anthropic. The course covers:
- Core concepts of MCP
- Client-server architecture
- Building MCP-compatible applications
- Connecting to third-party servers
- Deploying MCP servers remotely
For the complete course content, visit: https://learn.deeplearning.ai/courses/mcp-build-rich-context-ai-apps-with-anthropic
Contributing
This is a demo project for educational purposes. Feel free to experiment and modify the code to learn more about MCP implementation.
License
This project is for educational purposes only. Please refer to the original course materials for licensing information.
Support
For questions about the MCP protocol or the original course, please refer to: