# Implementing Model Context Protocol (MCP) Across AI Coding Assistants
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<div class="callout-title">Overview</div>
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Model Context Protocol (MCP) enables seamless integration between AI coding assistants and external tools or services. This article explores MCP implementation across Roo Code, Cline, and Cursor, demonstrating how to extend AI capabilities through standardized communication protocols.
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## Understanding MCP Architecture
The Model Context Protocol defines a standardized way for AI assistants to communicate with external services, enabling:
- Dynamic tool integration
- Resource access and management
- Stateful communication handling
- Cross-platform compatibility
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## MCP Server Types
MCP supports two primary server types:
| Server Type | Transport Method | Use Case |
|------------|-----------------|-----------|
| Local (Stdio) | Standard Input/Output | Local tools and services |
| Remote (SSE) | Server-Sent Events | Remote API integrations |
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<div class="callout-title">Implementation Strategy</div>
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Start with local Stdio servers for development and testing, then transition to SSE servers for production deployments. This approach allows for easier debugging and iteration.
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## Implementing MCP in Roo Code
### Server Configuration
```json
{
"mcpServers": {
"weather-service": {
"command": "node",
"args": ["/path/to/weather-server/build/index.js"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
```
### Tool Implementation
```typescript
class WeatherServer {
private server: Server;
constructor() {
this.server = new Server(
{
name: "weather-service",
version: "1.0.0"
},
{
capabilities: {
tools: {
getForecast: {
description: "Get weather forecast",
inputSchema: {
type: "object",
properties: {
location: { type: "string" }
}
}
}
}
}
}
);
}
}
```
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## Cline Integration
Cline supports MCP through its configuration system:
1. Global Configuration:
```json
{
"mcp": {
"servers": {
"custom-tools": {
"command": "node",
"args": ["./tools-server.js"],
"timeout": 30
}
}
}
}
```
2. Workspace-specific Configuration:
```json
{
"mcp": {
"allowedServers": ["custom-tools"],
"serverConfig": {
"custom-tools": {
"env": {
"WORKSPACE_PATH": "${workspaceFolder}"
}
}
}
}
}
```
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## Cursor Implementation
While Cursor doesn't natively support MCP, you can implement compatible servers:
```typescript
import { Server } from '@modelcontextprotocol/sdk';
class CursorCompatibleServer {
constructor() {
this.server = new Server({
name: "cursor-tools",
version: "1.0.0",
transport: "stdio"
});
this.setupHandlers();
}
private setupHandlers() {
this.server.onRequest("execute", async (params) => {
// Implementation
});
}
}
```
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<div class="callout-title">Common Pitfalls</div>
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- Ensure proper error handling in non-interactive environments
- Implement timeout handling for long-running operations
- Handle authentication and credentials securely
- Maintain proper state management across requests
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## Best Practices
### Security Considerations
1. Credential Management
- Use environment variables for sensitive data
- Implement proper token rotation
- Follow principle of least privilege
2. Error Handling
- Implement proper error boundaries
- Provide meaningful error messages
- Handle timeout scenarios
3. Performance Optimization
- Implement connection pooling
- Use proper caching strategies
- Handle concurrent requests efficiently
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## Example: Creating a Custom MCP Server
Here's a complete example of a custom MCP server that provides git operations:
```typescript
import { Server } from '@modelcontextprotocol/sdk';
import { exec } from 'child_process';
class GitServer {
private server: Server;
constructor() {
this.server = new Server({
name: "git-tools",
version: "1.0.0"
});
this.server.setRequestHandler("git-status", async () => {
return new Promise((resolve, reject) => {
exec('git status', (error, stdout, stderr) => {
if (error) reject(error);
resolve({ output: stdout });
});
});
});
}
async start() {
await this.server.connect(new StdioTransport());
console.log("Git MCP server running");
}
}
new GitServer().start().catch(console.error);
```
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<div class="callout-title">Key Takeaways</div>
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1. MCP provides a standardized way to extend AI coding assistants
2. Implementation can be tailored to specific needs while maintaining compatibility
3. Proper error handling and security measures are crucial
4. Different transport methods support various deployment scenarios
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## Further Reading
- [[Practical Applications/cline-roo-code-quick-start|Cline and Roo Code Quick Start Guide]]
- [[AI Systems & Architecture/custom-modes-quick-start|Creating Custom Modes in Roo Code]]
- [[AI Development & Agents/manus-im-vs-camel-ai-owl|Comparing AI Agent Platforms]]