Secure GitHub Automation with MCP Server: Step-by-Step Implementation Guide

Managing code changes, pull requests, and reviews is a daily challenge for development teams, especially when multiple developers collaborate. Manual handling of these tasks slows progress, introduces errors, and increases the risk of merge conflicts. 

Imagine if AI could commit code, review pull requests, resolve merge conflicts, and manage GitHub workflows — all securely and automatically. 

This is the goal of this project: building a secure AI-GitHub bridge using an MCP (Model Context Protocol) Server. The MCP Server acts as a smart, secure middle layer that ensures smooth automation while keeping credentials safe and compliance intact. 

Key Benefits: 

• Faster code operations with AI assistance 
• Reduced manual errors in PR reviews and merges 
• Secure token management and enterprise-compliant logging 

Understanding MCP Server

The MCP Server is the bridge between AI agents and external systems like GitHub. It ensures standardized, secure, and reliable communication between tools and services. 

Features 

• Secure Access Management: Encrypts GitHub tokens and credentials. 
• Unified API Gateway: Provides a consistent interface for different services. 
• Tool Exposure: Developers can expose functions as AI-callable tools. 
• Compliance & Logging: Tracks all actions for audit and enterprise standards. 

In short, MCP Server allows AI to interact with GitHub safely and efficiently, without adding integration complexity. 

Project Objective

Goal:

Build an MCP Server that securely connects to GitHub, allowing AI-driven automation to: 

• Commit and push code 
• Review and merge pull requests 
• Resolve merge conflicts 

Technology Stack: 

• Java 17+ (Spring Boot) 
• Gradle for build automation 
• Docker for cross-platform consistency 
• Jasypt for encrypted token storage 

Security Focus:

All credentials are encrypted; no secrets are stored in code or repositories. 

Pre-requisites

Project Setup and Implementation

1. Build Configuration

Add the following dependencies in build.gradle: 

dependencies { 
    implementation 'org.springframework.boot:spring-boot-starter-web' 
    implementation 'org.springframework.boot:spring-boot-starter-actuator' 
    implementation 'org.springframework.ai:spring-ai-starter-mcp-client:1.0.1' 
    implementation 'org.springframework.ai:spring-ai-starter-model-openai:1.0.1' 
    implementation 'org.kohsuke:github-api:1.316' 
    implementation 'com.github.ulisesbocchio:jasypt-spring-boot-starter:3.0.5' 
    implementation 'org.springframework.ai:spring-ai-starter-mcp-server' 
    testImplementation 'org.springframework.boot:spring-boot-starter-test' 
} 
  

Secure Configuration Setup 

Use Jasypt to encrypt tokens and configure the MCP Server: 

jasypt: 
  encryptor: 
    algorithm: PBEWithHMACSHA512AndAES_256 
 
mcp: 
  server: 
    url: http://localhost:3845/mcp 
  github: 
    token: ENC(encrypted-github-token) 
 
spring: 
  ai: 
    openai: 
      api-key: ENC(encrypted-openai-key) 
    mcp: 
      server: 
        name: github-automation-server 
        version: 1.0.0 
  main: 
    web-application-type: none 

Encrypting Tokens with Jasypt

• Encrypt your GitHub PAT using Jasypt CLI: 

jasypt encrypt input="GITHUB_PAT" password="your-secret

• Add the encrypted value to your configuration. 
• Provide the decryption password at runtime: 

java -Djasypt.encryptor.password=your-secret -jar github-automation.jar

GitHub Service Integration

Define MCP-callable GitHub functions using @Tool: 

@Service 
public class GithubMcpService { 
    private final GitHub github; 
 
    public GithubMcpService(@Value("${mcp.github.token}") String token) throws Exception { 
        this.github = GitHub.connectUsingOAuth(token); 
    } 
 
    @Tool(description = "Commits changes to a file in a repository.") 
    public String commitFile(String owner, String repoName, String filePath, String commitMessage, String branch) { 
        // Business logic using GitHub API 
        return "Commit successful. New SHA: [Generated SHA]"; 
    } 
 
    @Tool(description = "Merges a pull request after review.") 
    public String mergePullRequest(String owner, String repoName, int prNumber) { 
        // Example logic to merge PR securely 
        return "Pull request #" + prNumber + " merged successfully."; 
    } 
} 
  

MCP Server & AI Client Interaction

AI clients interact with the MCP Server through JSON-RPC, bypassing traditional REST endpoints. 

Workflow: 

• Plan & Request: AI Client sends JSON-RPC request with required parameters. 
• Execute: MCP Server runs the Java method. 
• Respond: MCP Server returns the result (e.g., commit ID) to the AI client. 

JSON-RPC Request Example: 

{ 
  "jsonrpc": "2.0", 
  "method": "commitFile", 
  "params": { 
    "owner": "neova-tech", 
    "repoName": "github-automation", 
    "filePath": "src/App.java", 
    "commitMessage": "Update README via AI", 
    "branch": "main" 
  }, 
  "id": 1 
} 

JSON-RPC Response Example: 

{ 
  "jsonrpc": "2.0", 
  "result": "Commit successful. New SHA: abcd1234", 
  "id": 1 
} 

Additional Use Cases

Neova’s Perspective: Applying MCP Automation in Product Development 

At Neova, we apply similar automation principles across our product development lifecycle. Our goal is to make AI a secure, compliant, and proactive assistant for every phase — not just code management. 

For example, by integrating an MCP Server with tools like Jira and internal QA systems, we enable AI agents to: 

• Draft user stories from meeting transcripts or feedback notes.   
• Trigger regression test suites based on pull request context.   
• Analyze failure logs to identify and categorize issues automatically.   

These secure, MCP-managed workflows ensure that every action — from code commits to test execution — is auditable, token-protected, and enterprise-ready. 

This same model supports our continuous integration and release management processes, where AI assists in code quality reviews, report generation, and automated release documentation — all without compromising security or compliance. 

By abstracting access through the MCP Server, we make sure our AI systems remain aligned with organizational security policies while improving speed, accuracy, and team collaboration across development, QA, and DevOps. 

Conclusion 

Using MCP Server, we can build a secure AI-driven GitHub automation system. Developers can now automate commits, pull requests, and merges without exposing credentials, improving speed, security, and productivity. 

This project demonstrates a robust approach to AI-assisted DevOps, combining Spring Boot, MCP Server, and Jasypt for secure and efficient automation. 

Next Steps: 

• Clone a sample repo and test the MCP GitHub automation. 
• Extend with more AI-assisted workflows, such as code quality reviews or automated release notes.