AI

January 12, 2025
in AI, MCP
4 min read

Writing My First MCP Server with Claude Code

Building my first Model Context Protocol (MCP) server was an exciting journey into extending Claude's capabilities. The MCP Memory Server allows Claude to store, retrieve, and manage memories across conversations, creating a persistent memory layer that enhances AI interactions.

What is MCP?

The Model Context Protocol (MCP) is an open standard that enables AI assistants like Claude to connect with external tools and data sources securely. It provides a standardized way to extend AI capabilities beyond their built-in knowledge, allowing for real-time data access and tool integration.

MCP enables: - Secure connections to external systems - Real-time data retrieval - Tool invocation and management - Standardized communication protocols

Project Overview

The MCP Memory Server is a lightweight Node.js application that provides Claude with persistent memory capabilities. Unlike traditional conversations that lose context when they end, this server allows Claude to:

Store important information for later retrieval
Tag memories for better organization
Search through stored memories
Maintain context across multiple conversations

Key Features

Memory Management Tools

The server implements five core tools that Claude can use:

Tool	Description	Input Parameters
store_memory	Save new memories with optional tags	`content` (string): Memory content `tags` (array): Optional categorization labels
retrieve_memories	Search and filter stored memories	Search criteria and filters
list_memories	View all stored memories	None
delete_memory	Remove specific memories by ID	`id` (string): Memory identifier
clear_memories	Delete all stored memories	None

Memory Structure

Each memory includes:

Unique ID: Auto-generated identifier
Content: The actual memory text
Tags: Optional categorization labels
Timestamp: Creation date and time

{
  "id": "unique-memory-id",
  "content": "The actual memory content",
  "tags": ["tag1", "tag2"],
  "timestamp": "2025-01-12T09:10:00Z"
}

Configuration and Setup

Server Configuration

The MCP Memory Server runs as a standalone Node.js application with configurable options:

// Environment Variables
const PORT = process.env.PORT || 3000;
const LOG_LEVEL = process.env.LOG_LEVEL || 'info';

// Server initialization
const server = new Server({
  name: 'memory-server',
  version: '1.0.0'
});

Claude Integration

Connecting the server to Claude Code is straightforward using the MCP HTTP transport:

# Option 1: Run locally
npm start

# Option 2: Run with Docker
docker build -t mcp-memory-server .
docker run -d -p 3000:3000 mcp-memory-server

# Connect to Claude
claude mcp add memory http://localhost:3000/message --transport http

Development Workflow

For development, the server supports hot reloading:

# Development mode with auto-restart
npm run dev

# Build and test
npm run build
npm test

How It Works

Server Architecture

The MCP Memory Server implements the MCP specification with these core components:

Tool Registry: Defines available memory operations
Message Handler: Processes incoming requests from Claude
Memory Store: In-memory storage for demonstration (extensible to databases)
Response Formatter: Structures responses according to MCP standards

Request Flow

sequenceDiagram
    participant Claude
    participant MCP Server
    participant Memory Store

    Claude->>MCP Server: store_memory request
    MCP Server->>Memory Store: Save memory
    Memory Store-->>MCP Server: Confirmation
    MCP Server-->>Claude: Success response

Memory Persistence

Currently, the server uses in-memory storage for simplicity, but it's designed for easy extension to persistent databases:

// Current implementation (in-memory)
const memories = new Map();

// Future extensions could use:
// - SQLite for local persistence
// - PostgreSQL for production
// - Redis for caching

Implementation Details

Tool Registration

Each tool is registered with the MCP server following the protocol specification:

server.setRequestHandler(ListToolsRequestSchema, async () => {
  return {
    tools: [
      {
        name: "store_memory",
        description: "Store a new memory with optional tags",
        inputSchema: {
          type: "object",
          properties: {
            content: { type: "string" },
            tags: { type: "array", items: { type: "string" } }
          },
          required: ["content"]
        }
      }
      // ... other tools
    ]
  };
});

Error Handling

The server implements comprehensive error handling:

try {
  // Tool execution logic
  const result = await executeMemoryOperation(args);
  return { content: [{ type: "text", text: result }] };
} catch (error) {
  return {
    content: [{
      type: "text",
      text: `Error: ${error.message}`
    }],
    isError: true
  };
}

Docker Deployment

For production deployment, the project includes Docker support:

FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm install
COPY . .
EXPOSE 3000
CMD ["npm", "start"]

Deploy with:

docker build -t mcp-memory-server .
docker run -d -p 3000:3000 mcp-memory-server

Future Enhancements

The current implementation serves as a foundation for more advanced features:

Planned Improvements

Database Integration: PostgreSQL or MongoDB for persistent storage
Advanced Search: Full-text search and semantic similarity
Memory Categories: Hierarchical organization system
Web Interface: Browser-based memory management
Authentication: Secure multi-user support
Memory Expiration: Automatic cleanup of old memories
Export/Import: Backup and migration capabilities

Scalability Considerations

Horizontal scaling with load balancers
Memory partitioning for large datasets
Caching layers for improved performance
Rate limiting and resource management

Lessons Learned

Building this MCP server taught me several valuable lessons:

MCP Protocol Benefits

Standardization: Consistent interface across different tools
Security: Built-in authentication and authorization
Flexibility: Easy to extend with new capabilities

Development Best Practices

Start Simple: Begin with core functionality before adding complexity
Error Handling: Robust error management is crucial for reliability
Documentation: Clear API documentation improves usability
Testing: Comprehensive tests ensure stability

Integration Challenges

Protocol Compliance: Strict adherence to MCP specifications
Performance: Balancing features with response times
User Experience: Making tools intuitive for Claude to use

Conclusion

Creating the MCP Memory Server was an excellent introduction to extending AI capabilities through the Model Context Protocol. The project demonstrates how developers can build powerful tools that enhance AI interactions while maintaining security and standardization.

The server successfully bridges the gap between Claude's conversational abilities and persistent data storage, opening up possibilities for more sophisticated AI workflows. Whether you're building internal tools or exploring AI extensibility, MCP provides a robust foundation for innovation.

For developers interested in MCP development, I encourage exploring the repository and experimenting with your own extensions. The future of AI tooling lies in these kinds of modular, interoperable systems.

Ready to build your own MCP server? Check out the complete source code and start extending Claude Code's capabilities today!