Quick-start Auto MCP
A tool that helps easily register Anthropic's Model Context Protocol (MCP) in Claude Desktop and Cursor, providing RAG functionality, Dify integration, and web search capabilities.
README
Quick-start Auto MCP : All in one Claude Desktop and Cursor
Introduction
Quick-start Auto MCP is a tool that helps you easily and quickly register Anthropic's Model Context Protocol (MCP) in Claude Desktop and Cursor.
Key advantages:
- Quick Setup: Add MCP functionality to Claude Desktop and Cursor simply by running a tool and copying/pasting the generated JSON file.
- Various Tools Provided: We continuously update useful MCP tools. Stay up to date with your personalized toolkit by starring and following us. :)
Table of Contents
- Features
- Project Structure
- Requirements
- Installation
- Configuration
- Usage
- Troubleshooting
- License
- Contributing
- Contact
- Author
Features
- RAG (Retrieval Augmented Generation) - Keyword, semantic, and hybrid search functionality for PDF documents
- Dify External Knowledge API - Document search functionality via Dify's external knowledge API
- Dify Workflow - Execute and retrieve results from Dify Workflow
- Web Search - Real-time web search using Tavily API
- Automatic JSON Generation - Automatically generate MCP JSON files needed for Claude Desktop and Cursor
Project Structure
.
├── case1 # RAG example
├── case2 # Dify External Knowledge API example
├── case3 # Dify Workflow example
├── case4 # Web Search example
├── data # Example data files
├── docs # Documentation folder
│ ├── case1.md # case1 description 🚨 Includes tips for optimized tool invocation
│ ├── case2.md # case2 description
│ ├── case3.md # case3 description
│ ├── case4.md # case4 description
│ └── installation.md # Installation guide
├── .env.example # .env example format
├── pyproject.toml # Project settings
├── requirements.txt # Required packages list
└── uv.lock # uv.lock
Requirements
- Python >= 3.11
- Claude Desktop or Cursor (MCP supporting version)
- uv (recommended) or pip
Installation
1. Clone the repository
git clone https://github.com/teddynote-lab/mcp.git
cd mcp
2. Set up virtual environment
Using uv (recommended)
# macOS/Linux
uv venv
uv pip install -r requirements.txt
# Windows
uv venv
uv pip install -r requirements_windows.txt
Using pip
python -m venv .venv
# Windows
.venv\Scripts\activate
pip install -r requirements_windows.txt
# macOS/Linux
source .venv/bin/activate
pip install -r requirements.txt
3. Preparing the PDF File
Plese prepare a PDF file required for RAG in the ./data directory.
Configuration
In order to execute each case, a .env file is required.
Please specify the necessary environment variables in the .env.example file located in the root directory, and rename it to .env.
sites for configuring required environment variables for each case
- https://platform.openai.com/api-keys
- https://dify.ai/
- https://app.tavily.com/home
Usage
1. Generate JSON File
Run the following command in each case directory to generate the necessary JSON file:
# Activate virtual environment
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate
# Navigate to example directory
cd case1
# Generate JSON file
python auto_mcp_json.py
2. Register MCP in Claude Desktop/Cursor
- Launch Claude Desktop or Cursor
- Open MCP settings menu
- Copy and paste the generated JSON content
- Save and
restart(If you're using Windows, we recommend fully closing the process via Task Manager and then restarting the application.)
Note: When you run Claude Desktop or Cursor, the MCP server will automatically run with it. When you close the software, the MCP server will also terminate.
Troubleshooting
Common issues and solutions:
- MCP Server Connection Failure: Check if the service is running properly and if there are no port conflicts. In particular, when applying case2, you must also run
dify_ek_server.py. - API Key Errors: Verify that environment variables are set correctly.
- Virtual Environment Issues: Ensure Python version is 3.11 or higher.
License
Contributing
Contributions are always welcome! Please participate in the project through issue registration or pull requests. :)
Contact
If you have questions or need help, please register an issue or contact: dev@brain-crew.com
Author
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.