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🚀 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>

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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:

# Install Turso CLI
curl -sSfL https://get.turso.tech/install.sh | bash

# Login to Turso
turso auth login

# Create a database
turso db create cursor10x-mcp

# Get database URL and token
turso db show cursor10x-mcp --url
turso db tokens create cursor10x-mcp

Or you can visit Turso and sign up and proceed to create the database and get proper credentials. The free plan will more than cover your project memory.

2. Configure Cursor MCP:

Update .cursor/mcp.json in your project directory with the database url and 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"
      }
    }
  }
}

Tool Documentation

System Tools

mcp_cursor10x_initConversation

Initializes a conversation by storing the user message, generating a banner, and retrieving context in one operation. This unified tool replaces the need for separate generateBanner, getComprehensiveContext, and storeUserMessage calls at the beginning of each conversation.

Parameters:

• content (string, required): Content of the user message
• importance (string, optional): Importance level ("low", "medium", "high", "critical"), defaults to "low"
• metadata (object, optional): Additional metadata for the message

Returns:

• Object with two sections:
  • display: Contains the banner to be shown to the user
  • internal: Contains the comprehensive context for the agent's use

Example:

// Initialize a conversation
const result = await mcp_cursor10x_initConversation({
  content: "I need to implement a login system for my app",
  importance: "medium",
});
// Result: {
//   "status": "ok",
//   "display": {
//     "banner": {
//       "status": "ok",
//       "memory_system": "active",
//       "mode": "turso",
//       "message_count": 42,
//       "active_files_count": 3,
//       "last_accessed": "4/15/2023, 2:30:45 PM"
//     }
//   },
//   "internal": {
//     "context": { ... comprehensive context data ... },
//     "messageStored": true,
//     "timestamp": 1681567845123
//   }
// }

mcp_cursor10x_endConversation

Ends a conversation by combining multiple operations in one call: storing the assistant's final message, recording a milestone for what was accomplished, and logging an episode in the episodic memory. This unified tool replaces the need for separate storeAssistantMessage, storeMilestone, and recordEpisode calls at the end of each conversation.

Parameters:

• content (string, required): Content of the assistant's final message
• milestone_title (string, required): Title of the milestone to record
• milestone_description (string, required): Description of what was accomplished
• importance (string, optional): Importance level ("low", "medium", "high", "critical"), defaults to "medium"
• metadata (object, optional): Additional metadata for all records

Returns:

• Object with status and results of each operation

Example:

// End a conversation with finalization steps
const result = await mcp_cursor10x_endConversation({
  content:
    "I've implemented the authentication system with JWT tokens as requested",
  milestone_title: "Authentication Implementation",
  milestone_description:
    "Implemented secure JWT-based authentication with refresh tokens",
  importance: "high",
});
// Result: {
//   "status": "ok",
//   "results": {
//     "assistantMessage": {
//       "stored": true,
//       "timestamp": 1681568500123
//     },
//     "milestone": {
//       "title": "Authentication Implementation",
//       "stored": true,
//       "timestamp": 1681568500123
//     },
//     "episode": {
//       "action": "completion",
//       "stored": true,
//       "timestamp": 1681568500123
//     }
//   }
// }

mcp_cursor10x_checkHealth

Checks the health of the memory system and its database connection.

Parameters:

• None required

Returns:

• Object with health status and diagnostics

Example:

// Check memory system health
const health = await mcp_cursor10x_checkHealth({});
// Result: {
//   "status": "ok",
//   "mode": "turso",
//   "message_count": 42,
//   "active_files_count": 3,
//   "current_directory": "/users/project",
//   "timestamp": "2023-04-15T14:30:45.123Z"
// }

mcp_cursor10x_getMemoryStats

Retrieves detailed statistics about the memory system.

Parameters:

• None required

Returns:

• Object with comprehensive memory statistics

Example:

// Get memory statistics
const stats = await mcp_cursor10x_getMemoryStats({});
// Result: {
//   "status": "ok",
//   "stats": {
//     "message_count": 42,
//     "active_file_count": 3,
//     "milestone_count": 7,
//     "decision_count": 12,
//     "requirement_count": 15,
//     "episode_count": 87,
//     "oldest_memory": "2023-03-10T09:15:30.284Z",
//     "newest_memory": "2023-04-15T14:30:45.123Z"
//   }
// }

mcp_cursor10x_getComprehensiveContext

Retrieves a unified context from all memory subsystems, combining short-term, long-term, and episodic memory.

Parameters:

• None required

Returns:

• Object with consolidated context from all memory systems

Example:

// Get comprehensive context
const context = await mcp_cursor10x_getComprehensiveContext({});
// Result: {
//   "status": "ok",
//   "context": {
//     "shortTerm": {
//       "recentMessages": [...],
//       "activeFiles": [...]
//     },
//     "longTerm": {
//       "milestones": [...],
//       "decisions": [...],
//       "requirements": [...]
//     },
//     "episodic": {
//       "recentEpisodes": [...]
//     },
//     "system": {
//       "healthy": true,
//       "timestamp": "2023-04-15T14:30:45.123Z"
//     }
//   }
// }

Short-Term Memory Tools

mcp_cursor10x_storeUserMessage

Stores a user message in the short-term memory