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MCP Server DevOps Bridge πŸš€

MCP Server DevOps Bridge πŸš€

Local
github.com/TheApeMachineDeveloper Tools2Updated 60w ago

About MCP Server DevOps Bridge πŸš€

MCP Server DevOps Bridge is a Go service for AI agents working with DevOps and communication systems. It combines Azure DevOps, GitHub, Slack, and nested-agent capabilities behind a unified MCP interface.

Key features

  1. 01Manage Azure DevOps work items and sprints
  2. 02Run WIQL queries
  3. 03Access GitHub repositories and pull requests
  4. 04Send messages and channel posts
  5. 05Create and manage nested agents

Use cases

  1. 01Update and track project work items
  2. 02Coordinate work through Slack messages
  3. 03Retrieve repository content during development
  4. 04Delegate tasks to nested agents

MCP support

It exposes DevOps, GitHub, communication, and agent-management capabilities as tools through MCP for AI agents and MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

πŸš€ MCP Server Multi Tools πŸŒ‰

Note

Model Context Protocol (MCP) Bridge - A powerful service that connects AI agents with DevOps and communication tools through a standardized interface.

Welcome to the MCP Server Multi Tools! This service acts as a seamless integration layer, enabling AI agents and systems to interact with services like Azure DevOps, Slack, GitHub, and even other agents (with their own tools) using a simple, unified protocol.


πŸ› οΈ Available Tools & Providers

πŸ“Š DevOps & Project Management

A comprehensive suite for total project management:

  • πŸ“ Work Items: Create, read, update, search, and comment
  • πŸƒβ€β™‚οΈ Sprints: Manage sprints, view contents, and track progress
  • πŸ” WIQL: Execute custom Work Item Query Language statements
  • πŸ”— Enrichment: Augment work items with GitHub, Slack, and Sentry context
  • πŸ“ Git Content: Fetch file content from associated repositories
πŸ™ GitHub Integration
  • Seamless version control and pull request workflows
  • Repository content access and management
πŸ’¬ Communication
  • Real-time notifications and messaging
  • Channel posting for agent communication
  • Team collaboration enhancement
πŸ€– Advanced Agent System

Important

Agents-in-Agents: Revolutionary nested agent architecture

Your AI agent can create and manage other agents, each with:

  • πŸ‹ Docker container with full Debian Linux system
  • 🌐 Web browser capabilities
  • πŸ”„ Iterative work processes
  • πŸ’¬ Inter-agent communication

πŸš€ Quick Start Guide

πŸ“‹ Prerequisites

Warning

Ensure you have the following before proceeding:

  • βœ… Go (latest version)
  • πŸ”‘ Service credentials (Azure DevOps, Slack tokens, etc.)
  • 🐳 Docker Desktop (for agents-in-agents feature)
Step 1️⃣: Clone & Build
# Clone the repository
git clone https://github.com/theapemachine/mcp-server-multi-tools.git
cd mcp-server-devops-bridge

# Build the server
go build -o mcp-server-multi-tools .
Step 2️⃣: Environment Setup
# Copy and configure environment variables
cp start.sh.example start.sh
vim start.sh  # Add your credentials

Tip

The server is configured entirely through environment variables for maximum flexibility.

Step 3️⃣: MCP Client Configuration

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "multi-tools": {
      "command": "/path/to/mcp-server-multi-tools/start.sh",
      "args": []
    }
  }
}

🎬 See It In Action

Note

Real AI Development Workflow - Watch Claude Sonnet 4 leverage the bridge to understand requirements, manage tasks, and write code.

AI Agent Development Example

The agent uses MCP tools to orchestrate complex development workflows through a single, unified interface.

For a complete history of testing, including the upgraded communication system and the headless browser tool, see agents-full.pdf.


🀝 Contributing

We welcome contributions! Here's how to get involved:

πŸ”„ Contribution Workflow
  1. 🍴 Fork the repository
  2. 🌿 Branch your feature (git checkout -b feature/AmazingFeature)
  3. πŸ’Ύ Commit your changes (git commit -m 'Add AmazingFeature')
  4. πŸš€ Push to branch (git push origin feature/AmazingFeature)
  5. πŸ“¬ Open a Pull Request
πŸ’‘ What We're Looking For
  • πŸ”§ New tool integrations
  • πŸ“š Documentation improvements
  • πŸ› Bug fixes and optimizations
  • 🎨 UI/UX enhancements

πŸ“œ License

MIT License - See LICENSE for details


Built with 🀷 for the developer community

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.