Cursor10x

by aurda012

80 stars
347 downloads
Not rated
GitHub

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

- 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:

  1. Download and install Highlight from highlightai.com/download
  2. Navigate to the plugins tab and select "Add Custom Plugin"
  3. Configure the plugin with the settings below
    Plugin Name Cursor10x
    Command (node, npx, python, etc.)

    Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.

  4. 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

No reviews yet — be the first

Sign in to leave a review

Use Google, GitHub, or an email account so ratings stay tied to real people.

Email sign in

No reviews posted yet.