Cursor10x
About
The Cursor10x MCP is a persistent multi-dimensional memory system for Cursor that enhances AI assistants with conversation context, project history, and code relationships across sessions.
Details
- Author
- aurda012
- GitHub stars
- 80
- Downloads
- 347
- Categories
- Database, AI, Developer Tools, Other
Jump to
- Persistent context across multiple sessions
- Importance-based information prioritization
- Multi-dimensional memory (STM, LTM, Episodic, Semantic)
- Vector embeddings for semantic similarity search
- Automatic code indexing and structure detection
- Health monitoring and built-in diagnostics
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Cursor10xCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install Node.js 18+ and create a Turso database account. Configure .cursor/mcp.json in your project with the Turso database URL and auth token. Then run the server via npx cursor10x-mcp. The AI assistant invokes the provided tools automatically.
generateBanner
Generates a banner containing memory system statistics and status
checkHealth
Checks the health of the memory system and its database
initConversation
Initializes a conversation by storing the user message, generating a banner, and retrieving context in one operation
endConversation
Ends a conversation by storing the assistant message, recording a milestone, and logging an episode in one operation
storeUserMessage
Stores a user message in the short-term memory
storeAssistantMessage
Stores an assistant message in the short-term memory
trackActiveFile
Tracks an active file being accessed by the user
getRecentMessages
Retrieves recent messages from the short-term memory
getActiveFiles
Retrieves active files from the short-term memory
storeMilestone
Stores a project milestone in the long-term memory
storeDecision
Stores a project decision in the long-term memory
storeRequirement
Stores a project requirement in the long-term memory
recordEpisode
Records an episode (action) in the episodic memory
getRecentEpisodes
Retrieves recent episodes from the episodic memory
getComprehensiveContext
Retrieves comprehensive context from all memory systems
getMemoryStats
Retrieves statistics about the memory system
manageVector
Unified tool for managing vector embeddings with operations for store, search, update, and delete
diagnoseVectors
Run diagnostics on the vector storage system to identify issues
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"cursor10x": {
"cursor10x-mcp": {
"command": "npx",
"args": [
"cursor10x-mcp"
],
"enabled": true,
"env": {
"TURSO_DATABASE_URL": "your-turso-database-url",
"TURSO_AUTH_TOKEN": "your-turso-auth-token"
}
}
}
}
}
McpServers
{
"cursor10x-mcp": {
"command": "npx",
"args": [
"cursor10x-mcp"
],
"enabled": true,
"env": {
"TURSO_DATABASE_URL": "your-turso-database-url",
"TURSO_AUTH_TOKEN": "your-turso-auth-token"
}
}
}
🚀 Cursor10x is now DevContext 🚀
Cursor10x has evolved into DevContext - A more powerful, dedicated context system for developers
<table align="center">
<tr>
<td align="center"><b>🧠 Project-Centric</b></td>
<td align="center"><b>📊 Relationship Graphs</b></td>
<td align="center"><b>⚡ High Performance</b></td>
</tr>
<tr>
<td align="center">One database per project</td>
<td align="center">Intelligent code connections</td>
<td align="center">Minimal resource needs</td>
</tr>
</table>
🔥 DevContext takes AI development to the next level 🔥
🔄 Continuous Context Awareness - Sophisticated retrieval methods focusing on what matters
📊 Structured Metadata - From repository structure down to individual functions
🧠 Adaptive Learning - Continuously learns from and adapts to your development patterns
🤖 Completely Autonomous - Self-managing context system that works in the background
📚 External Documentation - Automatically retrieves and integrates relevant documentation
📋 Workflow Integration - Seamless task management workflow built-in
👀 Be on the lookout 👀
The DevContext Project Generator is launching in the next couple days and will create a COMPLETE set up for your project to literally 10x your development workflow.
<p align="center">
<a href="https://github.com/aurda012/devcontext" style="display: inline-block; background-color: rgba(40, 230, 210); color: white; padding: 12px 24px; text-decoration: none; border-radius: 8px; font-weight: bold; box-shadow: 0 4px 6px rgba(0,0,0,0.1); transition: all 0.3s ease;">Visit DevContext Repository</a>
</p>
<i>DevContext is a cutting-edge Model Context Protocol (MCP) server providing developers with continuous, project-centric context awareness that understands your codebase at a deeper level.</i>
</div>
---
Overview
The Cursor10x Memory System creates a persistent memory layer for AI assistants (specifically Claude), enabling them to retain and recall:
- Recent messages and conversation history
- Active files currently being worked on
- Important project milestones and decisions
- Technical requirements and specifications
- Chronological sequences of actions and events (episodes)
- Code snippets and structures from your codebase
- Semantically similar content based on vector embeddings
- Related code fragments through semantic similarity
- File structures with function and variable relationships
This memory system bridges the gap between stateless AI interactions and continuous development workflows, allowing for more productive and contextually aware assistance.
System Architecture
The memory system is built on four core components:
1. MCP Server: Implements the Model Context Protocol to register tools and process requests
2. Memory Database: Uses Turso database for persistent storage across sessions
3. Memory Subsystems: Organizes memory into specialized systems with distinct purposes
4. Vector Embeddings: Transforms text and code into numerical representations for semantic search
Memory Types
The system implements four complementary memory types:
1. Short-Term Memory (STM)
- Stores recent messages and active files
- Provides immediate context for current interactions
- Automatically prioritizes by recency and importance
2. Long-Term Memory (LTM)
- Stores permanent project information like milestones and decisions
- Maintains architectural and design context
- Preserves high-importance information indefinitely
3. Episodic Memory
- Records chronological sequences of events
- Maintains causal relationships between actions
- Provides temporal context for project history
4. Semantic Memory
- Stores vector embeddings of messages, files, and code snippets
- Enables retrieval of content based on semantic similarity
- Automatically indexes code structures for contextual retrieval
- Tracks relationships between code components
- Provides similarity-based search across the codebase
Features
- Persistent Context: Maintains conversation and project context across multiple sessions
- Importance-Based Storage: Prioritizes information based on configurable importance levels
- Multi-Dimensional Memory: Combines short-term, long-term, episodic, and semantic memory systems
- Comprehensive Retrieval: Provides unified context from all memory subsystems
- Health Monitoring: Includes built-in diagnostics and status reporting
- Banner Generation: Creates informative context banners for conversation starts
- Database Persistence: Stores all memory data in Turso database with automatic schema creation
- Vector Embeddings: Creates numerical representations of text and code for similarity search
- Advanced Vector Storage: Utilizes Turso's F32_BLOB and vector functions for efficient embedding storage
- ANN Search: Supports Approximate Nearest Neighbor search for fast similarity matching
- Code Indexing: Automatically detects and indexes code structures (functions, classes, variables)
- Semantic Search: Finds related content based on meaning rather than exact text matches
- Relevance Scoring: Ranks context items by relevance to the current query
- Code Structure Detection: Identifies and extracts code components across multiple languages
- Auto-Embedding Generation: Automatically creates vector embeddings for indexed content
- Cross-Reference Retrieval: Finds related code across different files and components
Installation
Prerequisites
- Node.js 18 or higher
- npm or yarn package manager
- Turso database account
Setup Steps
1. Configure Turso Database:
```bash
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