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OnlineGDB MCP Server for C++ Code Execution

by ayushtiwari110
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OnlineGDB MCP Server for C++ Code Execution

A HTTP-based Model Context Protocol (MCP) server that enables AI agents to execute C++ code using OnlineGDB's online compiler. Perfect for solving Data Structures and Algorithms (DSA) problems from competitive programming sites like Codeforces, LeetCode, etc.

Note: This server uses HTTP transport instead of STDIO for better reliability and easier integration.

Features

Core Functionality

  • Execute C++ Code: Run C++ code with custom input using OnlineGDB compiler

  • Submit Solutions: Test complete solutions against multiple test cases

  • Code Optimization: Analyze code for performance improvements

  • Test Case Generation: Generate comprehensive test cases for problems

Agent Capabilities

  • Self-Correction: Agent can execute code, see errors, and iterate on solutions

  • Multiple Test Cases: Validate solutions against various inputs

  • Performance Analysis: Get execution time and memory usage feedback

  • Error Handling: Detailed compilation and runtime error reporting

Installation

  1. Clone and Setup

git clone <repository-url> cd onlinegdb-mcp-server npm install

  1. Build the Project

npm run build

  1. Install Dependencies The server requires Puppeteer for browser automation:

# On Ubuntu/Debian sudo apt-get update sudo apt-get install -y wget gnupg ca-certificates procps libxss1 sudo apt-get install -y libgconf-2-4 libxrandr2 libasound2 libpangocairo-1.0-0 libatk1.0-0 libcairo-gobject2 libgtk-3-0 libgdk-pixbuf2.0-0 libxcomposite1 libxcursor1 libxdamage1 libxi6 libxtst6 libnss3 libcups2 libxss1 libxrandr2 libasound2 libpangocairo-1.0-0 libatk1.0-0 libcairo-gobject2 libdrm2 libxkbcommon0 libgtk-3-0 # On macOS (using Homebrew) brew install chromium # On Windows # Puppeteer will download Chromium automatically

Usage

Starting the Server

# Build the project first npm run build # Start the HTTP MCP server npm start

The server will start on port 3000 (or PORT environment variable) and provide:

  • HTTP MCP Protocol endpoint: POST /mcp

  • REST API endpoints: GET /tools/list, GET /health

  • Web interface: GET /

Available Tools

1. execute_cpp_code

Execute C++ code with optional input:

{ "name": "execute_cpp_code", "arguments": { "code": "#include<iostream>\nusing namespace std;\nint main(){\n int n;\n cin >> n;\n cout << n * 2 << endl;\n return 0;\n}", "input": "5", "timeLimit": 5 } }

2. submit_solution

Test a complete solution against multiple test cases:

{ "name": "submit_solution", "arguments": { "problemName": "Two Sum", "code": "C++ solution code here", "testCases": [ { "input": "4\n2 7 11 15\n9", "expectedOutput": "0 1" }, { "input": "3\n3 2 4\n6", "expectedOutput": "1 2" } ], "functionSignature": "vector<int> twoSum(vector<int>& nums, int target)" } }

3. optimize_code

Analyze code for performance improvements:

{ "name": "optimize_code", "arguments": { "code": "C++ code to analyze", "constraints": { "timeComplexity": "O(n log n)", "spaceComplexity": "O(1)", "memoryLimit": "256MB" } } }

4. generate_test_cases

Generate test cases for a problem:

{ "name": "generate_test_cases", "arguments": { "problemDescription": "Find the maximum subarray sum", "constraints": "1 ≤ n ≤ 10^5, -10^9 ≤ arr[i] ≤ 10^9", "numTestCases": 5 } }

Integration with AI Agents

Claude MCP Configuration

Add to your Claude MCP configuration (use HTTP transport instead of STDIO):

Option 1: HTTP Transport (Recommended)

{ "mcpServers": { "onlinegdb-cpp": { "transport": { "type": "http", "url": "http://localhost:3000/mcp" } } } }

Option 2: Direct HTTP calls in your MCP client

// Make direct HTTP requests to the MCP endpoints fetch('http://localhost:3000/mcp', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ jsonrpc: "2.0", id: 1, method: "tools/list" }) })

Agent Workflow Example

  1. Problem Analysis: Agent receives a coding problem

  2. Solution Development: Agent writes initial C++ solution

  3. Code Execution: Agent uses execute_cpp_code to test basic functionality

  4. Comprehensive Testing: Agent uses submit_solution with multiple test cases

  5. Error Analysis: If tests fail, agent analyzes errors and iterates

  6. Optimization: Agent uses optimize_code to improve performance

  7. Final Validation: Agent runs final solution against all test cases

Example Agent Conversation

Human: Solve this problem: Given an array of integers, find two numbers that add up to a target sum. Agent: I'll solve this step by step using the OnlineGDB MCP server. First, let me implement a basic two-sum solution: [Uses execute_cpp_code to test basic logic] Now let me test it against multiple test cases: [Uses submit_solution with comprehensive test cases] The solution passed 4/5 test cases. Let me analyze the failure and optimize: [Uses optimize_code to identify issues] [Iterates and re-tests until all test cases pass]

Code Templates

Basic C++ Template for Competitive Programming

#include <iostream> #include <vector> #include <algorithm> #include <string> #include <map> #include <set> #include <queue> #include <stack> #include <cmath> using namespace std; int main() { ios_base::sync_with_stdio(false); cin.tie(NULL); // Your code here return 0; }

Function-Based Template

#include <iostream> #include <vector> using namespace std; // Function signature here vector<int> solve(vector<int>& arr, int target) { // Implementation return {}; } int main() { ios_base::sync_with_stdio(false); cin.tie(NULL); // Input parsing int n, target; cin >> n >> target; vector<int> arr(n); for(int i = 0; i < n; i++) { cin >> arr[i]; } // Call solution function vector<int> result = solve(arr, target); // Output formatting for(int i = 0; i < result.size(); i++) { cout << result[i]; if(i < result.size() - 1) cout << " "; } cout << endl; return 0; }

Testing

Run Comprehensive Tests

# Start the server in one terminal npm start # Run tests in another terminal node test-final-mcp.js # Or use the npm script npm run test:mcp

Manual Testing

# Test health endpoint curl http://localhost:3000/health # Test MCP initialize curl -X POST http://localhost:3000/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{"tools":{}},"clientInfo":{"name":"test","version":"1.0.0"}}}' # Test tools list curl -X POST http://localhost:3000/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":2,"method":"tools/list"}'

Troubleshooting

Common Issues

  1. Server won't start

    • Check if port 3000 is already in use

    • Set PORT environment variable: PORT=3001 npm start

    • Ensure dependencies are installed: npm install

  2. Puppeteer Installation Fails

    • Ensure you have the required system dependencies

    • Try npm install puppeteer --unsafe-perm=true

  3. OnlineGDB Not Loading

    • Check internet connection

    • OnlineGDB might be temporarily unavailable

    • Try increasing timeout limits

  4. Code Execution Timeouts

    • Increase timeLimit parameter

    • Check for

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