MCP Serena

MCP Serena

Overview and Purpose

MCP Serena is a comprehensive framework for integrating code-server instances with AI-assisted development capabilities. It provides a structured approach to managing cloud-based development environments, enabling collaborative coding with intelligent assistance.

The Serena MCP (Master Control Program) component enables developers to:

• Create and manage cloud-based development environments
• Leverage AI for code analysis, refactoring, and generation
• Maintain context awareness throughout development sessions
• Collaborate on code in real-time with team members
• Integrate with other VibeCode platform services
• Deploy and scale development environments in Kubernetes

By providing a seamless integration between code-server (a web-based VS Code environment) and AI services, MCP Serena creates a powerful development experience that combines the familiarity of VS Code with the intelligence of AI assistants.

Architecture and Components

MCP Serena consists of several key components that work together to provide a comprehensive development environment:

1. Serena Project Manager

The Project Manager handles project configuration, initialization, and management. It uses a project.yml file to define project-specific settings:

# language of the project
language: typescript

# whether to use the project's gitignore file to ignore files
ignore_all_files_in_gitignore: true

# list of additional paths to ignore
ignored_paths: []

# whether the project is in read-only mode
read_only: false

# list of tool names to exclude
excluded_tools: []

# initial prompt for the project
initial_prompt: ""

project_name: "vibecode-webgui"

The Project Manager provides functionality for:

• Project initialization and configuration
• File and directory management
• Project-specific settings and preferences
• Integration with version control

2. Serena Tool System

The Tool System provides a collection of tools for interacting with code and performing development tasks. These tools include:

• Code Analysis Tools: For understanding code structure and semantics
• Editing Tools: For modifying code with AI assistance
• Search Tools: For finding code patterns and symbols
• Execution Tools: For running code and tests
• Collaboration Tools: For working with team members

Each tool is designed to be used by both human developers and AI assistants, creating a consistent interface for interaction.

3. Serena Memory Store

The Memory Store provides persistent storage for context and knowledge across development sessions. It allows:

• Storing important project information for future reference
• Maintaining context between different sessions
• Sharing knowledge between team members
• Building a project-specific knowledge base over time

// Example of memory store usage
import { SerenaMemoryStore } from './serena-memory';

// Store important information
await SerenaMemoryStore.write('project-structure', {
  entryPoints: ['src/index.ts'],
  mainComponents: ['App', 'Dashboard', 'CodeEditor'],
  testingFramework: 'Jest',
});

// Retrieve information in a later session
const projectStructure = await SerenaMemoryStore.read('project-structure');
console.log('Main components:', projectStructure.mainComponents);

4. Code-Server Client

The Code-Server Client manages the lifecycle of code-server instances, providing an API for creating, accessing, and managing development environments:

import { CodeServerClient } from '../lib/code-server-client';

// Create a new code-server session
const session = await codeServerClient.createSession(workspaceId, userId);

// Get an existing session or create a new one
const session = await codeServerClient.getOrCreateSession(workspaceId, userId);

// List active sessions
const { sessions } = await codeServerClient.listSessions(workspaceId);

// Delete a session
await codeServerClient.deleteSession(sessionId);

5. Kubernetes Integration

MCP Serena integrates with Kubernetes for deploying and scaling code-server instances. This integration includes:

• Deployment Templates: YAML configurations for deploying code-server
• Service Definitions: For exposing code-server to users
• Ingress Rules: For handling external access
• Resource Management: For controlling CPU, memory, and storage

apiVersion: apps/v1
kind: Deployment
metadata:
  name: vibecode-code-server
  namespace: vibecode-platform
spec:
  replicas: 1
  selector:
    matchLabels:
      app: vibecode-code-server
  template:
    metadata:
      labels:
        app: vibecode-code-server
    spec:
      containers:
      - name: code-server
        image: vibecode/code-server:latest
        ports:
        - containerPort: 8080
        volumeMounts:
        - name: workspace
          mountPath: /home/coder/workspace

Integration with Code-Server

MCP Serena integrates with code-server to provide a complete web-based development environment. This integration includes:

1. Customized Code-Server Configuration

MCP Serena provides custom configuration for code-server instances, including:

• Extensions: Pre-installed VS Code extensions for development
• Settings: Optimized settings for AI-assisted development
• Themes: Custom themes for a consistent experience
• Keybindings: Configurable keybindings for different preferences

2. Authentication and Authorization

Serena integrates with the VibeCode authentication system to provide secure access to code-server instances:

private async fetchWithAuth(endpoint: string, options: RequestInit = {}) {
  const session = await getSession();
  const headers = new Headers(options.headers);

  // Use the session token if available in the session
  const token = session?.user?.email;
  if (token) {
    headers.set('Authorization', `Bearer ${token}`);
  }

  headers.set('Content-Type', 'application/json');

  const response = await fetch(`${this.baseUrl}${endpoint}`, {
    ...options,
    headers,
    credentials: 'include',
  });

  if (!response.ok) {
    const error = await response.json().catch(() => ({}));
    throw new Error(error.message || 'Failed to fetch');
  }

  return response.json();
}

3. Workspace Management

Serena provides tools for managing workspaces, including:

• Workspace Creation: Creating new development workspaces
• Workspace Configuration: Configuring workspaces for specific projects
• Workspace Sharing: Sharing workspaces with team members
• Workspace Persistence: Ensuring workspaces persist across sessions

4. File Synchronization

Serena ensures files are synchronized between the local and remote environments:

• Real-time Sync: Changes are synchronized in real-time
• Conflict Resolution: Conflicts are detected and resolved
• Version Control Integration: Integration with Git for tracking changes
• Backup and Recovery: Automatic backup of workspace state

Configuration and Setup

Setting Up MCP Serena

To set up MCP Serena for a new project:

1. Create a .serena directory in your project root:

mkdir -p .serena

2. Create a project.yml file in the .serena directory:

language: typescript
ignore_all_files_in_gitignore: true
ignored_paths: []
read_only: false
excluded_tools: []
initial_prompt: "This is a TypeScript project using Next.js and React."
project_name: "my-project"

3. Configure code-server integration in your environment:

src/config/serena.ts

export const serenaConfig = {
  codeServer: {
    baseUrl: process.env.CODE_SERVER_URL || 'https://code.example.com',
    defaultTimeout: 30000,
    resources: {
      cpu: '1',
      memory: '2Gi',
      storage: '10Gi',
    },
  },
  ai: {
    model: 'claude-3-opus-20240229',
    contextWindow: 200000,
    temperature: 0.7,
  },
};

4. Set up Kubernetes deployment files:

k8s/serena-code-server.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: serena-code-server
spec:
  replicas: 1
  selector:
    matchLabels:
      app: serena-code-server
  template:
    metadata:
      labels:
        app: serena-code-server
    spec:
      containers:
      - name: code-server
        image: serena/code-server:latest
        env:
        - name: SERENA_PROJECT
          value: "my-project"
        - name: SERENA_AI_ENDPOINT
          valueFrom:
            secretKeyRef:
              name: serena-secrets
              key: ai-endpoint

Environment Variables

MCP Serena uses several environment variables for configuration:

Variable	Description	Default
SERENA_PROJECT	The current project name	-
SERENA_AI_ENDPOINT	Endpoint for AI services	https://ai.vibecode.example.com
SERENA_CODE_SERVER_URL	Base URL for code-server	https://code.vibecode.example.com
SERENA_MEMORY_STORE	Storage backend for memory	redis
SERENA_LOG_LEVEL	Logging level	info

Usage Examples and Workflows

Example 1: Setting Up a New Project

import { SerenaProjectManager } from '../lib/serena';

// Initialize a new project
const project = await SerenaProjectManager.initialize({
  name: 'my-typescript-project',
  language: 'typescript',
  description: 'A TypeScript project with React and Next.js',
});

// Create a code-server session for the project
const session = await codeServerClient.createSession(project.id, currentUser.id);

// Open the project in the browser
window.open(session.url, '_blank');

Example 2: AI-Assisted Code Analysis

import { SerenaCodeAnalyzer } from '../lib/serena';

// Analyze a file with AI assistance
const analysis = await SerenaCodeAnalyzer.analyzeFile({
  filePath: 'src/components/Dashboard.tsx',
  options: {
    checkComplexity: true,
    suggestImprovements: true,
    identifyPatterns: true,
  },
});

console.log('Code complexity:', analysis.complexity);
console.log('Suggested improvements:', analysis.improvements);
console.log('Identified patterns:', analysis.patterns);

Example 3: Collaborative Development

import { SerenaCollaboration } from '../lib/serena';

// Create a collaborative session
const collaborationSession = await SerenaCollaboration.createSession({
  projectId: 'my-project',
  participants: ['user1@example.com', 'user2@example.com'],
  permissions: {
    'user1@example.com': 'admin',
    'user2@example.com': 'editor',
  },
});

// Share the session with participants
await SerenaCollaboration.sendInvitations(collaborationSession.id);

// Monitor activity in the session
SerenaCollaboration.onActivityUpdate(collaborationSession.id, (activity) => {
  console.log(`${activity.user} ${activity.action} at ${activity.timestamp}`);
});

Example 4: AI-Assisted Refactoring

import { SerenaRefactoring } from '../lib/serena';

// Refactor a component with AI assistance
const refactoringResult = await SerenaRefactoring.refactorComponent({
  filePath: 'src/components/UserProfile.tsx',
  options: {
    extractComponents: true,
    improvePerformance: true,
    updateStandardsCompliance: true,
  },
});

console.log('Refactoring changes:', refactoringResult.changes);
console.log('Performance improvements:', refactoringResult.performanceImprovements);

Example 5: Code Generation

import { SerenaCodeGenerator } from '../lib/serena';

// Generate a new component with AI assistance
const generatedCode = await SerenaCodeGenerator.generateComponent({
  name: 'DataVisualization',
  description: 'A component that visualizes time-series data with interactive charts',
  props: [
    { name: 'data', type: 'TimeSeriesData[]', required: true },
    { name: 'title', type: 'string', required: false },
    { name: 'showLegend', type: 'boolean', required: false, default: true },
  ],
  styleFramework: 'styled-components',
});

// Write the generated code to a file
await SerenaCodeGenerator.writeToFile(
  'src/components/DataVisualization.tsx',
  generatedCode
);

Integration with Context7 and Sequential Thinking

Integration with Context7

MCP Serena integrates with Context7 to maintain rich contextual information during development sessions:

import { Context7Manager } from '../lib/context7';
import { SerenaSession } from '../lib/serena';

// Create a Serena session with Context7 integration
const session = new SerenaSession({
  projectId: 'my-project',
  contextManager: new Context7Manager({
    temporal: {
      sessionStartTime: Date.now(),
      actionHistory: [],
    },
    spatial: {
      currentLocation: 'src/components/Dashboard.tsx',
      navigationPath: ['src', 'components', 'Dashboard.tsx'],
    },
    state: {
      applicationState: {
        currentBranch: 'feature/dashboard-redesign',
        uncommittedChanges: true,
      },
    },
    user: {
      userId: 'developer@example.com',
      profile: {
        expertise: 'intermediate',
        theme: 'dark',
      },
    },
  }),
});

// Use context in Serena operations
await session.executeOperation('analyzeCode', {
  filePath: 'src/components/Dashboard.tsx',
  // Context7 automatically provides:
  // - User expertise level
  // - Recent file navigation history
  // - Current application state
  // - Previous interactions
});

Integration with Sequential Thinking

MCP Serena integrates with Sequential Thinking to structure complex development tasks:

import { SequentialThinkingProcess } from '../lib/sequential-thinking';
import { SerenaTask } from '../lib/serena';

// Create a Serena task with Sequential Thinking
const task = new SerenaTask({
  name: 'Implement user authentication',
  description: 'Add user authentication to the application',
  thinkingProcess: new SequentialThinkingProcess(),
});

// Define the sequential steps
task.thinkingProcess.addThought(
  'Analyze the current codebase to understand the existing structure and dependencies',
  1,
  7
);

task.thinkingProcess.addThought(
  'Design the authentication system with considerations for security and user experience',
  2,
  7
);

task.thinkingProcess.addThought(
  'Implement the authentication API endpoints for login, registration, and logout',
  3,
  7
);

task.thinkingProcess.addThought(
  'Create the frontend components for the authentication forms',
  4,
  7
);

task.thinkingProcess.addThought(
  'Implement state management for authentication status',
  5,
  7
);

task.thinkingProcess.addThought(
  'Add protected routes and access control',
  6,
  7
);

task.thinkingProcess.addThought(
  'Test the authentication system and fix any issues',
  7,
  7
);

// Execute the task
await task.execute();

Combined Integration Example

The true power of MCP Serena comes when combining Context7 and Sequential Thinking:

import { Context7Manager } from '../lib/context7';
import { SequentialThinkingProcess } from '../lib/sequential-thinking';
import { SerenaProject } from '../lib/serena';

// Create a Serena project with integrated context and thinking
const project = new SerenaProject({
  name: 'e-commerce-platform',
  contextManager: new Context7Manager(),
  thinkingProcess: new SequentialThinkingProcess(),
});

// Start a complex development task
await project.startTask({
  name: 'Implement shopping cart feature',
  description: 'Add a fully featured shopping cart with persistence, item management, and checkout integration',
  async execute() {
    // Step 1: Analyze existing codebase
    this.thinkingProcess.addThought(
      'Analyze the existing codebase to understand structure and integration points',
      1,
      5
    );

    const codebaseAnalysis = await project.analyzeCodebase({
      focusAreas: ['product catalog', 'user session', 'state management'],
    });

    this.contextManager.updateStateContext({
      applicationState: {
        ...this.contextManager.getStateContext().applicationState,
        codebaseAnalysis,
      },
    });

    // Step 2: Design the shopping cart model
    this.thinkingProcess.addThought(
      'Design the shopping cart data model and persistence strategy',
      2,
      5
    );

    const cartModel = await project.generateModel({
      name: 'ShoppingCart',
      properties: [
        { name: 'id', type: 'string', required: true },
        { name: 'userId', type: 'string', required: true },
        { name: 'items', type: 'CartItem[]', required: true },
        { name: 'createdAt', type: 'Date', required: true },
        { name: 'updatedAt', type: 'Date', required: true },
      ],
      relations: [
        { name: 'user', type: 'User', relation: 'belongsTo' },
        { name: 'items', type: 'CartItem', relation: 'hasMany' },
      ],
    });

    this.contextManager.updateSemanticContext({
      entities: new Map([
        ...Array.from(this.contextManager.getSemanticContext().entities.entries()),
        ['ShoppingCart', { type: 'model', attributes: { properties: cartModel.properties } }],
      ]),
    });

    // Step 3: Implement backend services
    this.thinkingProcess.addThought(
      'Implement backend services for cart management',
      3,
      5
    );

    await project.generateService({
      name: 'ShoppingCartService',
      methods: [
        { name: 'getCart', params: [{ name: 'userId', type: 'string' }], returnType: 'ShoppingCart' },
        { name: 'addItem', params: [{ name: 'userId', type: 'string' }, { name: 'productId', type: 'string' }, { name: 'quantity', type: 'number' }], returnType: 'ShoppingCart' },
        { name: 'removeItem', params: [{ name: 'userId', type: 'string' }, { name: 'itemId', type: 'string' }], returnType: 'ShoppingCart' },
        { name: 'updateQuantity', params: [{ name: 'userId', type: 'string' }, { name: 'itemId', type: 'string' }, { name: 'quantity', type: 'number' }], returnType: 'ShoppingCart' },
        { name: 'clearCart', params: [{ name: 'userId', type: 'string' }], returnType: 'void' },
      ],
    });

    this.contextManager.recordUserAction('implemented_cart_service');

    // Step 4: Implement frontend components
    this.thinkingProcess.addThought(
      'Implement frontend components for cart interaction',
      4,
      5
    );

    await project.generateComponent({
      name: 'ShoppingCart',
      props: [
        { name: 'isOpen', type: 'boolean', required: true },
        { name: 'onClose', type: 'function', required: true },
      ],
      state: [
        { name: 'items', type: 'CartItem[]', initialValue: '[]' },
        { name: 'isLoading', type: 'boolean', initialValue: 'false' },
        { name: 'error', type: 'string | null', initialValue: 'null' },
      ],
      hooks: [
        { name: 'useShoppingCart', params: [] },
        { name: 'useUser', params: [] },
      ],
    });

    this.contextManager.recordUserAction('implemented_cart_component');

    // Step 5: Integrate with checkout
    this.thinkingProcess.addThought(
      'Integrate shopping cart with checkout flow',
      5,
      5
    );

    await project.modifyComponent({
      name: 'CheckoutFlow',
      changes: [
        { type: 'import', import: 'useShoppingCart', from: '../hooks/useShoppingCart' },
        { type: 'addHook', hook: 'const { items, total } = useShoppingCart();' },
        { type: 'addSection', section: 'cart-summary', content: `
          <div className="cart-summary">
            <h3>Order Summary</h3>
            {items.map(item => (
              <div key={item.id} className="cart-item-summary">
                <span>{item.product.name}</span>
                <span>{item.quantity} x ${item.product.price}</span>
                <span>${item.quantity * item.product.price}</span>
              </div>
            ))}
            <div className="cart-total">
              <strong>Total:</strong> ${total}
            </div>
          </div>
        `},
      ],
    });

    this.contextManager.recordUserAction('integrated_cart_with_checkout');

    // Update context with completion
    this.contextManager.updateTaskContext({
      completedTasks: [...this.contextManager.getTaskContext().completedTasks, 'implement-shopping-cart'],
    });
  },
});

Deployment and Scaling

Kubernetes Deployment

MCP Serena can be deployed to Kubernetes for scalability and high availability:

k8s/serena-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: serena-platform
  namespace: vibecode-platform
spec:
  replicas: 3
  selector:
    matchLabels:
      app: serena-platform
  template:
    metadata:
      labels:
        app: serena-platform
    spec:
      containers:
      - name: serena-api
        image: vibecode/serena-api:latest
        ports:
        - containerPort: 3000
        env:
        - name: SERENA_AI_ENDPOINT
          valueFrom:
            secretKeyRef:
              name: serena-secrets
              key: ai-endpoint
        - name: SERENA_MEMORY_STORE
          value: "redis"
        - name: REDIS_HOST
          value: "redis-master.vibecode-platform.svc.cluster.local"
        resources:
          requests:
            cpu: "500m"
            memory: "1Gi"
          limits:
            cpu: "2"
            memory: "4Gi"

Dynamic Scaling

MCP Serena supports dynamic scaling of code-server instances based on demand:

k8s/serena-hpa.yaml

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: serena-code-server-hpa
  namespace: vibecode-platform
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: serena-code-server
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

Cloud Provider Integration

MCP Serena integrates with major cloud providers for seamless deployment:

• AWS: Integration with EKS, ECR, and other AWS services
• Azure: Integration with AKS, Container Registry, and other Azure services
• Google Cloud: Integration with GKE, Container Registry, and other GCP services

// Example of AWS integration
import { SerenaCloud } from '../lib/serena';
import { AWS } from '../lib/cloud-providers';

// Deploy Serena to AWS
await SerenaCloud.deploy({
  provider: AWS,
  region: 'us-west-2',
  cluster: 'vibecode-cluster',
  options: {
    autoScaling: true,
    loadBalancer: true,
    monitoring: true,
  },
});

Advanced Features and Customization

Custom Tools Integration

MCP Serena supports integration with custom tools:

import { SerenaToolRegistry } from '../lib/serena';

// Register a custom tool
SerenaToolRegistry.register({
  name: 'code-quality-analyzer',
  description: 'Analyzes code quality using custom metrics',
  execute: async (filePath, options) => {
    // Implementation of the tool
    const fileContent = await fs.readFile(filePath, 'utf-8');
    const analysis = analyzeCodeQuality(fileContent, options);
    return analysis;
  },
});

// Use the custom tool
const quality = await SerenaToolRegistry.execute('code-quality-analyzer', {
  filePath: 'src/components/Dashboard.tsx',
  metrics: ['complexity', 'maintainability', 'duplication'],
});

Language Server Integration

MCP Serena integrates with language servers for enhanced code intelligence:

import { SerenaLanguageServer } from '../lib/serena';

// Configure a language server
await SerenaLanguageServer.configure({
  language: 'typescript',
  serverPath: '/usr/local/bin/typescript-language-server',
  args: ['--stdio'],
  features: {
    completion: true,
    definition: true,
    references: true,
    diagnostics: true,
  },
});

// Use the language server for code intelligence
const references = await SerenaLanguageServer.findReferences({
  filePath: 'src/components/Dashboard.tsx',
  position: { line: 42, character: 15 },
});

Custom Templates

MCP Serena supports custom templates for code generation:

import { SerenaTemplateManager } from '../lib/serena';

// Register a custom template
await SerenaTemplateManager.register({
  name: 'react-component',
  description: 'Template for React components',
  template: `
import React, { {{#if hasState}}useState{{/if}} } from 'react';
{{#if hasProps}}
interface {{name}}Props {
  {{#each props}}
  {{name}}{{#unless required}}?{{/unless}}: {{type}};
  {{/each}}
}
{{/if}}

export const {{name}} = ({{#if hasProps}}props: {{name}}Props{{else}}{}{{/if}}) => {
  {{#if hasState}}
  {{#each state}}
  const [{{name}}, set{{capitalName}}] = useState<{{type}}>({{initialValue}});
  {{/each}}
  {{/if}}

  return (
    <div>
      {{defaultContent}}
    </div>
  );
};
`,
});

// Use the template
const component = await SerenaTemplateManager.generate('react-component', {
  name: 'UserProfile',
  hasProps: true,
  props: [
    { name: 'user', type: 'User', required: true },
    { name: 'showDetails', type: 'boolean', required: false },
  ],
  hasState: true,
  state: [
    { name: 'isExpanded', type: 'boolean', initialValue: 'false', capitalName: 'IsExpanded' },
  ],
  defaultContent: '<h2>{props.user.name}</h2>',
});

AI Model Customization

MCP Serena allows customization of AI models for different tasks:

import { SerenaAI } from '../lib/serena';

// Configure AI models
await SerenaAI.configureModels({
  default: 'claude-3-opus-20240229',
  codeGeneration: 'claude-3-opus-20240229',
  codeAnalysis: 'claude-3-sonnet-20240229',
  documentation: 'claude-3-haiku-20240307',
  chat: 'claude-3-sonnet-20240229',
});

// Use a specific model for a task
const analysis = await SerenaAI.analyzeCode({
  code: 'const foo = () => { console.log("Hello"); }',
  model: 'codeAnalysis', // Uses the configured model for code analysis
  options: {
    detailed: true,
    suggestImprovements: true,
  },
});

Conclusion

MCP Serena provides a comprehensive framework for integrating code-server with AI-assisted development capabilities. By combining the power of cloud-based development environments with advanced AI services, it creates a seamless and productive development experience.

The integration with other MCP components like Context7 and Sequential Thinking enhances its capabilities, allowing for context-aware, structured development workflows. With its flexible deployment options and customization features, MCP Serena can be adapted to fit a wide range of development needs and environments.

By leveraging MCP Serena, developers can focus on creating high-quality code while the AI handles routine tasks, provides intelligent suggestions, and maintains context throughout the development process.