Neo4j, Model Context Protocol (MCP) Server and Docker Desktop

In today’s AI landscape, managing interactions between applications and language models can be complex and inconsistent. The Model Context Protocol (MCP) aims to solve this by providing a standardized way to handle these interactions. Let’s dive into what MCP is and why you might need it in your projects.

What is MCP?

The Model Context Protocol is a standardized specification for managing context and interactions between applications and AI models. Think of it as a universal translator that ensures your application and AI models speak the same language consistently.

The Model Context Protocol (MCP) is an emerging standard for managing and communicating context between AI models and applications. The Model Context Protocol is a standardized way of handling context in AI model interactions. It provides a structured approach to:

• Managing model capabilities and constraints
• Managing model state and context
• Handling conversation history
• Standardizing input/output formats

Why Do You Need MCP?

1. Standardized Context Management

Without MCP, each application might handle model context differently:

// Without MCP - Inconsistent approaches
const app1Context = {
    history: [],
    addMessage(msg) { this.history.push(msg); }
};

const app2Context = {
    messages: [],
    appendChat(content) { this.messages.push({time: Date.now(), text: content}); }
};

With MCP, you get a standardized approach:

// With MCP - Standardized context management
class ModelContext {
    constructor() {
        this.conversation = [];
        this.capabilities = new Set();
    }

    addMessage(role, content) {
        this.conversation.push({
            role,
            content,
            timestamp: new Date().toISOString()
        });
    }
}

2. Capability Management

MCP helps you clearly define and check what your AI model can and cannot do:

const context = new ModelContext();
context.capabilities.add('text-generation');
context.capabilities.add('code-completion');

// Easy capability checking
if (context.hasCapability('image-generation')) {
    // Handle image generation
} else {
    // Handle unsupported capability
}

3. Error Handling and Recovery

MCP provides consistent error-handling patterns:

try {
    await mcp.process(userInput);
} catch (error) {
    if (error instanceof MCPValidationError) {
        // Handle invalid inputs
    } else if (error instanceof MCPCapabilityError) {
        // Handle unsupported capabilities
    } else if (error instanceof MCPContextLimitError) {
        // Handle context length limits
    }
}

4. Cross-Model Compatibility

One of the biggest advantages of MCP is its ability to work with different AI models:

// Same context format works with different models
const openAIContext = new ModelContext();
const claudeContext = new ModelContext();
const llamaContext = new ModelContext();

// They all use the same protocol
async function processWithAnyModel(input, model, context) {
    return await model.generate(input, context);
}

5. State Management and Persistence

MCP provides clear patterns for managing and persisting state:

class MCPStateManager {
    async saveContext(context) {
        const serialized = context.serialize();
        await database.save(serialized);
    }

    async loadContext(contextId) {
        const data = await database.load(contextId);
        return ModelContext.deserialize(data);
    }
}

In this blog post, you’ll see how to deploy an MCP server that provides integration between Neo4j graph database and Claude Desktop, enabling graph database operations through natural language interactions.

Getting Started

Step 1: Install Neo4j Docker Extension

Step 2. Login using Neo4j user and password as password.

Step 3. Load the following movie database.

Step 4. You will see the graph relationship and nodes

Step 5. Install Claude Desktop

Step 6. Open claude_desktop_config.json file and add the following:

I have multiple MCP server here – GITHUB as well as Neo4j.

Quit and restart Claude to see the Neo4j tool under Claude Desktop.

Step 7. Start with the natural prompt.

Prompt #1:  List out all the movies acted by Keanu ReevesPrompt #2: Change all the directors of Keanue Reeves movies to Ajeet Raina

Imagine you have a GitHub repository that has code that fetches sensor values from the BME680 device, push it to the Neo4j graph database and creates a Grafana dashboard. All this can be implemented using prompt engineering.

Conclusion: The Future of AI Integration with MCP

The Model Context Protocol represents more than just another development standard – it’s a fundamental shift in how we think about AI integration in modern applications. As we’ve explored throughout this post, MCP addresses several critical challenges:

• It simplifies the complexity of managing AI model interactions
• It provides a consistent, reliable way to handle context across different models
• It future-proofs applications against the rapidly evolving AI landscape
• It reduces development time and potential errors through standardization

Looking Forward

As AI continues to evolve at a breakneck pace, having a standardized protocol like MCP becomes increasingly valuable. Whether you’re building a simple chatbot or a complex enterprise AI system, MCP provides the foundation needed to:

• Scale your applications confidently
• Switch between different AI models seamlessly
• Maintain clean, consistent codebases
• Reduce technical debt
• Improve team collaboration through standardized practices