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 contenttags (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:
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 responseMemory 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:
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!