Vector Memory MCP Server

A secure, vector-based memory server for Claude Desktop using sqlite-vec and sentence-transformers. This MCP server provides persistent semantic memory capabilities that enhance AI coding assistants by remembering and retrieving relevant coding experiences, solutions, and knowledge.

✨ Features

• 🔍 Semantic Search: Vector-based similarity search using 384-dimensional embeddings
• 💾 Persistent Storage: SQLite database with vector indexing via sqlite-vec
• 🏷️ Smart Organization: Categories and tags for better memory organization
• 🔒 Security First: Input validation, path sanitization, and resource limits
• ⚡ High Performance: Fast embedding generation with sentence-transformers
• 🧹 Auto-Cleanup: Intelligent memory management and cleanup tools
• 📊 Rich Statistics: Comprehensive memory database analytics
• 🔄 Automatic Deduplication: SHA-256 content hashing prevents storing duplicate memories
• 📈 Access Tracking: Monitors memory usage with access counts and timestamps for optimization
• 🧠 Smart Cleanup Algorithm: Prioritizes memory retention based on recency, access patterns, and importance

🛠️ Technical Stack

Component	Technology	Purpose
Vector DB	sqlite-vec	Vector storage and similarity search
Embeddings	sentence-transformers/all-MiniLM-L6-v2	384D text embeddings
MCP Framework	FastMCP	High-level tools-only server
Dependencies	uv script headers	Self-contained deployment
Security	Custom validation	Path/input sanitization
Testing	pytest + coverage	Comprehensive test suite

📁 Project Structure

vector-memory-mcp/
├── main.py                              # Main MCP server entry point
├── README.md                            # This documentation
├── requirements.txt                     # Python dependencies
├── pyproject.toml                       # Modern Python project config
├── .python-version                      # Python version specification
├── claude-desktop-config.example.json  # Claude Desktop config example
│
├── src/                                # Core package modules
│   ├── __init__.py                    # Package initialization
│   ├── models.py                      # Data models & configuration
│   ├── security.py                    # Security validation & sanitization
│   ├── embeddings.py                  # Sentence-transformers wrapper
│   └── memory_store.py                # SQLite-vec operations
│
└── .gitignore                         # Git exclusions

🗂️ Organization Guide

This project is organized for clarity and ease of use:

• main.py - Start here! Main server entry point
• src/ - Core implementation (security, embeddings, memory store)
• claude-desktop-config.example.json - Configuration template

New here? Start with main.py and claude-desktop-config.example.json

🚀 Quick Start

Prerequisites

• Python 3.10 or higher (recommended: 3.11)
• uv package manager
• Claude Desktop app

Installing uv (if not already installed):

macOS and Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Verify installation:

uv --version

Installation

Option 1: Quick Install via uvx (Recommended)

The easiest way to use this MCP server - no cloning or setup required!

Once published to PyPI, you can use it directly:

# Run without installation (like npx)
uvx vector-memory-mcp --working-dir /path/to/your/project

Claude Desktop Configuration (using uvx):

{
  "mcpServers": {
    "vector-memory": {
      "command": "uvx",
      "args": [
        "vector-memory-mcp",
        "--working-dir",
        "/absolute/path/to/your/project",
        "--memory-limit",
        "100000"
      ]
    }
  }
}

Note: --memory-limit is optional. Omit it to use default 10,000 entries.

Note: Publishing to PyPI is in progress. See PUBLISHING.md for details.

Option 2: Install from Source (For Development)

1. Clone the project:

   git clone <repository-url>
   cd vector-memory-mcp
   

2. Install dependencies (automatic with uv): Dependencies are automatically managed via inline metadata in main.py. No manual installation needed.

   To verify dependencies:

   uv pip list
   

3. Test the server:

   # Test with sample working directory
   uv run main.py --working-dir ./test-memory
   

4. Configure Claude Desktop:

   Copy the example configuration:

   cp claude-desktop-config.example.json ~/path/to/your/config/
   

   Open Claude Desktop Settings → Developer → Edit Config, and add (replace paths with absolute paths):

   {
     "mcpServers": {
       "vector-memory": {
         "command": "uv",
         "args": [
           "run",
           "/absolute/path/to/vector-memory-mcp/main.py",
           "--working-dir",
           "/your/project/path",
           "--memory-limit",
           "100000"
         ]
       }
     }
   }
   

   Important:

   • Use absolute paths, not relative paths
   • --memory-limit is optional (default: 10,000)
   • For large projects, use 100,000-1,000,000

5. Restart Claude Desktop and look for the MCP integration icon.

Option 3: Install with pipx (Alternative)

# Install globally (once published to PyPI)
pipx install vector-memory-mcp

# Run
vector-memory-mcp --working-dir /path/to/your/project

Claude Desktop Configuration (using pipx):

{
  "mcpServers": {
    "vector-memory": {
      "command": "vector-memory-mcp",
      "args": [
        "--working-dir",
        "/absolute/path/to/your/project",
        "--memory-limit",
        "100000"
      ]
    }
  }
}

📚 Usage Guide

Available Tools

1. store_memory - Store Knowledge

Store coding experiences, solutions, and insights:

Please store this memory:
Content: "Fixed React useEffect infinite loop by adding dependency array with [userId, apiKey]. The issue was that the effect was recreating the API call function on every render."
Category: bug-fix
Tags: ["react", "useEffect", "infinite-loop", "hooks"]

2. search_memories - Semantic Search

Find relevant memories using natural language:

Search for: "React hook dependency issues"

3. list_recent_memories - Browse Recent

See what you've stored recently:

Show me my 10 most recent memories

4. get_memory_stats - Database Health

View memory database statistics:

Show memory database statistics

5. clear_old_memories - Cleanup

Clean up old, unused memories:

Clear memories older than 30 days, keep max 1000 total

6. get_by_memory_id - Retrieve Specific Memory

Get full details of a specific memory by its ID:

Get memory with ID 123

Returns all fields including content, category, tags, timestamps, access count, and metadata.

7. delete_by_memory_id - Delete Memory

Permanently remove a specific memory from the database:

Delete memory with ID 123

Removes the memory from both metadata and vector tables atomically.

Memory Categories

Category	Use Cases
code-solution	Working code snippets, implementations
bug-fix	Bug fixes and debugging approaches
architecture	System design decisions and patterns
learning	New concepts, tutorials, insights
tool-usage	Tool configurations, CLI commands
debugging	Debugging techniques and discoveries
performance	Optimization strategies and results
security	Security considerations and fixes
other	Everything else

🔧 Configuration

Command Line Arguments

The server supports the following arguments:

# Run with uv (recommended) - default 10,000 memory limit
uv run main.py --working-dir /path/to/project

# With custom memory limit for large projects
uv run main.py --working-dir /path/to/project --memory-limit 100000

# Working directory is where memory database will be stored
uv run main.py --working-dir ~/projects/my-project --memory-limit 500000

Available Options:

• --working-dir (required): Directory where memory database will be stored
• --memory-limit (optional): Maximum number of memory entries
  • Default: 10,000 entries
  • Minimum: 1,000 entries
  • Maximum: 10,000,000 entries
  • Recommended for large projects: 100,000-1,000,000

Working Directory Structure

your-project/
├── memory/
│   └── vector_memory.db    # SQLite database with vectors
├── src/                    # Your project files
└── other-files...

Security Limits

• Max memory content: 10,000 characters
• Max total memories: Configurable via --memory-limit (default: 10,000 entries)
• Max search results: 50 per query
• Max tags per memory: 10 tags
• Path validation: Blocks suspicious characters

🎯 Use Cases

For Individual Developers

# Store a useful code pattern
"Implemented JWT refresh token logic using axios interceptors"

# Store a debugging discovery  
"Memory leak in React was caused by missing cleanup in useEffect"

# Store architecture decisions
"Chose Redux Toolkit over Context API for complex state management because..."

For Team Workflows

# Store team conventions
"Team coding style: always use async/await instead of .then() chains"

# Store deployment procedures
"Production deployment requires running migration scripts before code deploy"

# Store infrastructure knowledge
"AWS RDS connection pooling settings for high-traffic applications"

For Learning & Growth

# Store learning insights
"Understanding JavaScript closures: inner functions have access to outer scope"

# Store performance discoveries
"Using React.memo reduced re-renders by 60% in the dashboard component"

# Store security learnings
"OWASP Top 10: Always sanitize user input to prevent XSS attacks"

🔍 How Semantic Search Works

The server uses sentence-transformers to convert your memories into 384-dimensional vectors that capture semantic meaning:

Example Searches

Query	Finds Memories About
"authentication patterns"	JWT, OAuth, login systems, session management
"database performance"	SQL optimization, indexing, query tuning, caching
"React state management"	useState, Redux, Context API, state patterns
"API error handling"	HTTP status codes, retry logic, error responses

Similarity Scoring

• 0.9+ similarity: Extremely relevant, almost exact matches
• 0.8-0.9: Highly relevant, strong semantic similarity
• 0.7-0.8: Moderately relevant, good contextual match
• 0.6-0.7: Somewhat relevant, might be useful
• <0.6: Low relevance, probably not helpful

📊 Database Statistics

The get_memory_stats tool provides comprehensive insights:

{
  "total_memories": 247,
  "memory_limit": 100000,
  "usage_percentage": 0.25,
  "categories": {
    "code-solution": 89,
    "bug-fix": 67,
    "learning": 45,
    "architecture": 23,
    "debugging": 18,
    "other": 5
  },
  "recent_week_count": 12,
  "database_size_mb": 15.7,
  "health_status": "Healthy"
}

Statistics Fields Explained

• total_memories: Current number of memories stored in the database
• memory_limit: Maximum allowed memories (configurable via --memory-limit, default: 10,000)
• usage_percentage: Database capacity usage (total_memories / memory_limit * 100)
• categories: Breakdown of memory count by category type
• recent_week_count: Number of memories created in the last 7 days
• database_size_mb: Physical size of the SQLite database file on disk
• health_status: Overall database health indicator based on usage and performance metrics

🛡️ Security Features

Input Validation

• Sanitizes all user input to prevent injection attacks
• Removes control characters and null bytes
• Enforces length limits on all content

Path Security

• Validates and normalizes all file paths
• Prevents directory traversal attacks
• Blocks suspicious character patterns

Resource Limits

• Limits total memory count and individual memory size
• Prevents database bloat and memory exhaustion
• Implements cleanup mechanisms for old data

SQL Safety

• Uses parameterized queries exclusively
• No dynamic SQL construction from user input
• SQLite WAL mode for safe concurrent access

🔧 Troubleshooting

Common Issues

Server Not Starting

# Check if uv is installed
uv --version

# Test server manually
uv run main.py --working-dir ./test

# Check Python version
python --version  # Should be 3.10+

Claude Desktop Not Connecting

1. Verify absolute paths in configuration
2. Check Claude Desktop logs: ~/Library/Logs/Claude/
3. Restart Claude Desktop after config changes
4. Test server manually before configuring Claude

Memory Search Not Working

• Verify sentence-transformers model downloaded successfully
• Check database file permissions in memory/ directory
• Try broader search terms
• Review memory content for relevance

Performance Issues

• Run get_memory_stats to check database health
• Use clear_old_memories to clean up old entries
• Consider increasing hardware resources for embedding generation

Debug Mode

Run the server manually to see detailed logs:

uv run main.py --working-dir ./debug-test

🚀 Advanced Usage

Batch Memory Storage

Store multiple related memories by calling the tool multiple times through Claude Desktop interface.

Memory Organization Strategies

By Project

Use tags to organize by project:

• ["project-alpha", "frontend", "react"]
• ["project-beta", "backend", "node"]
• ["project-gamma", "devops", "docker"]

By Technology Stack

• ["javascript", "react", "hooks"]
• ["python", "django", "orm"]
• ["aws", "lambda", "serverless"]

By Problem Domain

• ["authentication", "security", "jwt"]
• ["performance", "optimization", "caching"]
• ["testing", "unit-tests", "mocking"]

Integration with Development Workflow

Code Review Learnings

"Code review insight: Extract validation logic into separate functions for better testability and reusability"

Sprint Retrospectives

"Sprint retrospective: Using feature flags reduced deployment risk and enabled faster rollbacks"

Technical Debt Tracking

"Technical debt: UserService class has grown too large, needs refactoring into smaller domain-specific services"

📈 Performance Benchmarks

Based on testing with various dataset sizes:

Memory Count	Search Time	Storage Size	RAM Usage
1,000	<50ms	~5MB	~100MB
5,000	<100ms	~20MB	~200MB
10,000	<200ms	~40MB	~300MB

Tested on MacBook Air M1 with sentence-transformers/all-MiniLM-L6-v2

🔧 Advanced Implementation Details

Database Indexes

The memory store uses 4 optimized indexes for performance:

1. idx_category: Speeds up category-based filtering and statistics
2. idx_created_at: Optimizes temporal queries and recent memory retrieval
3. idx_content_hash: Enables fast deduplication checks via SHA-256 hash lookups
4. idx_access_count: Improves cleanup algorithm efficiency by tracking usage patterns

Deduplication System

Content deduplication uses SHA-256 hashing to prevent storing identical memories:

• Hash calculated on normalized content (trimmed, lowercased)
• Check performed before insertion
• Duplicate attempts return existing memory ID
• Reduces storage overhead and maintains data quality

Access Tracking

Each memory tracks usage statistics for intelligent management:

• access_count: Number of times memory retrieved via search or direct access
• last_accessed_at: Timestamp of most recent access
• created_at: Original creation timestamp
• Used by cleanup algorithm to identify valuable vs. stale memories

Cleanup Algorithm

Smart cleanup prioritizes memory retention based on multiple factors:

1. Recency: Newer memories are prioritized over older ones
2. Access patterns: Frequently accessed memories are protected
3. Age threshold: Configurable days_old parameter for hard cutoff
4. Count limit: Maintains max_memories cap by removing least valuable entries
5. Scoring system: Combines access_count and recency for retention decisions

🤝 Contributing

This is a standalone MCP server designed for personal/team use. For improvements:

1. Fork the repository
2. Modify as needed for your use case
3. Test thoroughly with your specific requirements
4. Share improvements via pull requests

📄 License

This project is released under the MIT License.

🙏 Acknowledgments

• sqlite-vec: Alex Garcia's excellent SQLite vector extension
• sentence-transformers: Nils Reimers' semantic embedding library
• FastMCP: Anthropic's high-level MCP framework
• Claude Desktop: For providing the MCP integration platform

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Built for developers who want persistent AI memory without the complexity of dedicated vector databases.