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Rag Code MCP
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Rag Code MCP

RagCode MCP is a privacy-first local AI code assistant that enables AI assistants to understand the entire codebase through semantic vector search and RAG technology. It supports multiple languages such as Go, PHP, and Python, without relying on the cloud.
Developer toolsArtificial intelligence chatbots#Semantic code search#Local AI assistant#Intelligent code navigation#Privacy-first.Go
2.5 points
4.4K
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Overview

Content Details

Alternatives

What is RagCode MCP?

RagCode MCP is a locally running AI code assistant server that allows AI tools (such as GitHub Copilot, Cursor, Windsurf, Claude) to understand your entire codebase through semantic vector search technology. It automatically analyzes the code structure and builds semantic indexes, enabling AI to quickly find relevant code snippets and answer questions about the code.

How to use RagCode MCP?

After installation, simply open your project in a supported IDE and then ask the AI assistant questions about the code. RagCode will automatically index the code in the background and provide intelligent answers on your first query. No additional configuration is required; it's ready to use out of the box.

Use cases

Suitable for developers who need to quickly understand large codebases, to understand others' code during team collaboration, to analyze dependencies before code refactoring, to learn new project architectures, and for enterprise environments that need to protect code privacy.

Main Features

Semantic code search
Search by understanding the meaning of the code through AI, rather than simple keyword matching. It can find code with similar functionality, even if the naming is different.
Multi-language support
Supports multiple programming languages such as Go, PHP (including Laravel), Python, etc., and automatically analyzes code structures such as functions, classes, and interfaces.
9 intelligent tools
Provides 9 dedicated tools, including semantic search, hybrid search, function details, type definition, implementation lookup, etc., covering various code query needs.
100% locally running
All processing is done locally, and the code never leaves your machine, ensuring complete privacy and security.
Zero-configuration usage
It can be used right after installation. It automatically detects the workspace and does not require complex configuration, making it suitable for quick onboarding.
Multi-IDE integration
Supports mainstream AI development tools such as Windsurf, Cursor, VS Code + Copilot, and Claude Desktop.
Advantages
๐Ÿ”’ Complete privacy protection - Code is processed 100% locally and not sent to the cloud
๐Ÿ’ฐ Zero cost - No API fees, free to use permanently
โšก 5 - 10 times speed improvement - Much faster than manual code searching
๐Ÿ“Š 98% token savings - AI only needs to read relevant code, reducing context length
๐ŸŒ Available offline - No network connection required after installation
๐Ÿ”ง Intelligent code understanding - Search based on semantics rather than keywords
Limitations
๐Ÿ’พ Requires local resources - Needs more than 16GB of RAM and sufficient disk space
โฑ๏ธ First indexing takes time - The first analysis of a large project may take a few minutes
๐Ÿ”Œ Depends on Docker - Requires Docker to run the Qdrant vector database
๐Ÿค– Model size limitation - The local AI model's capabilities are limited compared to large cloud models
๐ŸŒ Limited language support - Currently mainly supports Go, PHP, Python

How to Use

One-click installation
Run the corresponding installation command according to your operating system, and the installer will automatically download the required components.
Open the project
Open your code project in an IDE that supports MCP, such as Windsurf, Cursor, or VS Code.
Start asking questions
Ask the AI assistant questions about the code, and RagCode will automatically process and return the answers.

Usage Examples

Understand the authentication system
When you need to quickly understand the user authentication and authorization mechanism of a project
Find API endpoints
When you need to quickly understand all API interfaces in a project
Analyze data models
When you want to understand the database table structure and model relationships

Frequently Asked Questions

Does RagCode require an internet connection?
Which programming languages are supported?
How much system resources are required?
Will the code be sent to the cloud?
How to update RagCode?

Related Resources

Quick Start Guide
Detailed installation and configuration steps
GitHub Repository
Source code and issue tracking
Configuration Guide
Advanced configuration and customization options
Troubleshooting
Solutions to common problems
Model Context Protocol Official Website
Official documentation of the MCP protocol

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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