Roblox Documentation MCP Server
Enables AI agents to intelligently search and retrieve Roblox documentation through semantic search and vector embeddings, providing natural language access to complete Roblox Creator Documentation.
README
🚀 Roblox Documentation MCP Server with RAG Support
MCP server with RAG support for intelligent Roblox documentation search and retrieval
This MCP server enables AI agents to intelligently search and retrieve Roblox documentation through semantic search and vector embeddings. It provides natural language access to the complete Roblox Creator Documentation.
🎯 What This Does
Enable AI agents to:
- 🔍 Semantic Search: Find relevant documentation through natural language queries
- 📚 API References: Get specific details about Roblox classes, methods, and properties
- 🎓 Tutorial Discovery: Locate step-by-step guides and learning materials
- 💡 Code Examples: Find relevant code snippets and demonstrations
- 🏷️ Smart Filtering: Search by content type, difficulty, or topic
🏗️ Architecture
graph TD
A[AI Agent] --> B[MCP Server]
B --> C[RAG Service]
C --> D[ChromaDB Vector Store]
C --> E[OpenAI Embeddings]
B --> F[Git Service]
F --> G[Roblox/creator-docs Repository]
B --> H[Content Processor]
H --> I[Markdown Parser]
H --> J[YAML Parser]
✨ Key Features
| Feature Area | Description | Implementation |
|---|---|---|
| 🔍 Semantic Search | Natural language queries across all Roblox documentation | ChromaDB + OpenAI embeddings |
| 📖 Content Processing | Processes markdown guides, tutorials, and YAML API references | markdown-it + yaml parsers |
| 🔄 Auto-Updates | Keeps documentation current via git pull from official repository | simple-git integration |
| 🏷️ Smart Classification | Automatically categorizes content (guides, tutorials, API references) | Metadata extraction + classification |
| ⚡ Performance | Fast semantic search with caching and optimized vector storage | Redis caching + ChromaDB |
| 🔒 Production Ready | Built on proven MCP template with comprehensive error handling | Full TypeScript + Zod validation |
🚀 Quick Start
Prerequisites
- Node.js 20+
- ChromaDB server (Docker recommended)
- OpenAI API key
Installation
# Clone the repository
git clone https://github.com/christopher-buss/roblox-docs-mcp.git
cd roblox-docs-mcp
# Install dependencies
npm install
# Set up environment variables
cp .env.example .env
# Edit .env with your OpenAI API key and ChromaDB settings
# Start ChromaDB (using Docker)
docker run -p 8000:8000 chromadb/chroma
# Build the project
npm run build
Environment Configuration
Create a .env file with the following variables:
# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key
# ChromaDB Configuration
CHROMA_DB_URL=http://localhost:8000
CHROMA_DB_COLLECTION=roblox-docs
# Roblox Documentation
ROBLOX_DOCS_REPO_URL=https://github.com/Roblox/creator-docs.git
ROBLOX_DOCS_LOCAL_PATH=./data/roblox-docs
ROBLOX_DOCS_UPDATE_INTERVAL=24
# Embedding Configuration
EMBEDDING_MODEL=text-embedding-3-large
MAX_CHUNK_SIZE=1000
CHUNK_OVERLAP=200
# Optional Redis Cache
REDIS_URL=redis://localhost:6379
Running the Server
# Start MCP server (stdio transport)
npm run start:stdio
# Start MCP server (HTTP transport)
npm run start:http
# Update documentation repository
npm run docs:update
# Launch MCP inspector for debugging
npm run inspector
🛠️ Available MCP Tools
searchRobloxDocs
Purpose: Semantic search across all Roblox documentation
Input: Natural language query, optional filters
Output: Ranked list of relevant documentation with metadata
getRobloxApiReference
Purpose: Get specific API class/method documentation
Input: API name, class name, method name
Output: Detailed API documentation with examples
findRobloxTutorials
Purpose: Find step-by-step tutorials and guides
Input: Topic, difficulty level, tutorial type
Output: Curated list of tutorials with descriptions
getRobloxGuides
Purpose: Retrieve conceptual guides and explanations
Input: Topic area, content type
Output: Relevant guides with structured content
📁 Project Structure
src/
├── services/
│ ├── git-service/ # Git repository operations
│ ├── content-processor/ # Markdown/YAML processing
│ └── roblox-rag/ # RAG implementation
├── mcp-server/
│ ├── tools/ # MCP tools for documentation search
│ └── server.ts # Main MCP server
├── config/ # Configuration management
└── utils/ # Production utilities
🔧 Development
Architecture Overview
This project extends the cyanheads/mcp-ts-template with Roblox-specific capabilities:
- Git Service: Manages the Roblox creator-docs repository
- Content Processor: Parses markdown and YAML files
- RAG Service: Handles embeddings and semantic search
- MCP Tools: Provides search and retrieval capabilities
Adding New Features
- New Tools: Follow the template pattern in
src/mcp-server/tools/ - Content Processing: Extend processors in
src/services/content-processor/ - RAG Enhancements: Modify search logic in
src/services/roblox-rag/
Development Commands
npm run build # Build TypeScript
npm run format # Format code with Prettier
npm run docs:generate # Generate TypeDoc documentation
npm run tree # Generate project structure
npm run depcheck # Check for unused dependencies
🧪 Testing
# Test individual components
npm run test:unit
# Test MCP tools end-to-end
npm run test:integration
# Test RAG functionality
npm run test:rag
📊 Performance
- Search Latency: < 500ms for semantic queries
- Memory Usage: < 2GB RAM for full documentation index
- Document Processing: 100+ docs/minute ingestion rate
- Cache Hit Rate: > 80% for repeated queries
🤝 Contributing
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests if applicable
- Submit a pull request
📜 License
This project is licensed under the Apache License 2.0. See the LICENSE file for details.
🙏 Acknowledgments
- Built on the excellent cyanheads/mcp-ts-template
- Powered by Roblox Creator Documentation
- Uses ChromaDB for vector storage
- Embeddings by OpenAI
📚 Documentation
- Development Plan - Complete implementation roadmap
- Context for Agents - Guide for AI development
- Claude Configuration - Claude Code specific guidance
Note: This project is currently in development. See DEVELOPMENT_PLAN.md for current status and implementation progress.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。