What is Model Context Protocol (MCP) and what problem it solves?

LLM is everywhere—powering chatbots, crunching data, helping with decision-making. But if you’ve ever tried scaling one in an enterprise setting, you’ve probably hit a few walls.

Context management is one of the biggest headaches. Keeping track of all the moving parts—user inputs, historical data, and domain-specific knowledge—gets messy fast. That’s where the Model Context Protocol (MCP) steps in.

What problem does MCP fixes?

Imagine you’re using ChatGPT or AI Chatbot. Suddenly it forgets what you just said. That’s contextual drift. LLMs can lose track of historical or domain-specific context during inference, and it messes up the output.

How many times did you encountered weird, unpredictable responses from your model? Yeah, that’s because there’s no framework keeping everything consistent.

How MCP solves these problem?

MCP’s stateful context management system keeps all the important info in check. It tracks what matters, so your model doesn’t lose its way. MCP keeps tabs on dependencies, prioritizes updates, and makes sure critical context sticks around during interactions. MCP prevents context loss, so your model’s responses actually make sense—even under pressure.

MCP ensures outputs stay aligned with the provided context. Consistency means better reliability and trust.

How does MCP works?

The Model Context Protocol (MCP) is designed to handle complex context management in a smart and efficient way. It uses real-time state synchronization to ensure that Large Language Models (LLMs) stay context-aware across various tools and data sources.

The MCP acts as a standardized middleware layer that seamlessly connects Large Language Models (LLMs) with the external systems they rely on, such as databases, APIs, and other tools. This protocol layer is designed to address one of the biggest challenges in enterprise AI applications: managing the flow of context between the model and its environment.

Here’s a high-level breakdown of how it works:

From a security perspective, MCP provides strong access controls with detailed permissions and audit trails. This keeps sensitive data safe while maintaining high system performance. It also includes built-in monitoring tools, making it easier to track the flow of context and quickly resolve any issues in production.

How Developers Benefit from MCP?

For developers, MCP simplifies integration work with its SDK-first approach. Instead of creating custom integrations for each new tool or data source, developers can use standardized interfaces, saving time and effort.

MCP’s stateful connection management ensures reliable performance, even as systems scale. Its distributed architecture efficiently manages increasing loads without requiring major changes, eliminating common bottlenecks seen in custom-built solutions.

By offering this comprehensive infrastructure, MCP transforms how LLM applications are developed and deployed. It allows teams to focus on building core features rather than wrestling with complex integration challenges, resulting in more robust and maintainable AI systems.

MCP also addresses key challenges like standardization, interoperability, security, and scalability, providing a solid foundation for enterprise AI. It enables smoother workflows, connects tools like Claude to various systems, and helps save time by streamlining processes.

As more teams adopt MCP, their hands-on experience will contribute to refining and improving the protocol. This collaboration will ensure MCP continues to evolve to meet the growing demands of AI workflows, while maintaining reliability and performance.

Getting Started

The mcp_server_git is an incredibly powerful tool for integrating Git repository interactions with Large Language Models (LLMs). By leveraging Docker, you can streamline the process of setting up and running mcp_server_git to enable tools for managing Git repositories programmatically. This blog will guide you through the steps to get started with mcp_server_git using Docker and showcase its tools.

What is mcp_server_git?

The mcp_server_git is a Model Context Protocol server designed to automate and interact with Git repositories. It enables tools such as git_status, git_diff, git_commit, and git_show to integrate seamlessly with LLM workflows, offering developers an efficient way to manage repositories programmatically.

Key features include:

• Reading and searching Git repositories.
• Manipulating commits, branches, and diffs programmatically.
• Automation of common Git tasks like staging, committing, and branching.

Step 1: Clone the Repository

To get started with the source code, clone the repository:

git clone https://github.com/modelcontextprotocol/servers.git
cd servers/src/git

This will bring the mcp_server_git source code to your local machine.

Step 2: Build and Run Using Docker

Option A: Build the Docker Image

If you want to build the Docker image locally:

1. Navigate to the src/git directory.
2. Build the Docker image:docker build -t mcp/git .
3. Run the container:

docker run -d --name mcp-git -p 8080:8080 mcp/git

Option B: Pull Prebuilt Docker Image

If the image is already available on Docker Hub, you can pull and run it directly:

docker pull mcp/git
docker run -d --name mcp-git -p 8080:8080 mcp/git

This will start the mcp_server_git container, exposing it on port 8080.

Step 3: Using mcp_server_git Tools

Once the server is up and running, you can use its tools via HTTP requests. Here are some examples of its capabilities:

1. Get Git Status

Check the working tree status of a repository:

curl -X POST http://localhost:8080/git_status \
  -H "Content-Type: application/json" \
  -d '{"repo_path": "/path/to/repo"}'

2. View Unstaged Changes

Fetch changes in the working directory that are not yet staged:

curl -X POST http://localhost:8080/git_diff_unstaged \
  -H "Content-Type: application/json" \
  -d '{"repo_path": "/path/to/repo"}'

3. Commit Changes

Stage changes and commit them with a message:

curl -X POST http://localhost:8080/git_commit \
  -H "Content-Type: application/json" \
  -d '{"repo_path": "/path/to/repo", "message": "Initial commit"}'

4. Show Commit Content

Retrieve details of a specific commit:

curl -X POST http://localhost:8080/git_show \
  -H "Content-Type: application/json" \
  -d '{"repo_path": "/path/to/repo", "revision": "commit_hash"}'

Step 4: Debugging

If the server does not respond as expected, here’s how you can debug:

1. Check Docker Logs

Get logs from the running container:

docker logs mcp-git

2. Use MCP Inspector

For deeper insights, use the MCP inspector (requires uvx):

npx @modelcontextprotocol/inspector uvx mcp-server-git

Step 5: Integration with Development Tools

Claude Desktop Integration

To integrate with Claude Desktop, update your configuration file (claude_desktop_config.json):

{
  "mcpServers": {
    "git": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-p", "8080:8080",
        "--mount", "type=bind,src=/path/to/repo,dst=/projects/repo",
        "mcp/git"
      ]
    }
  }
}

Zed Integration

To integrate with Zed, update your settings.json:

{
  "mcpServers": {
    "git": {
      "command": "uvx",
      "args": [
        "--directory",
        "/path/to/git/server",
        "run",
        "mcp-server-git"
      ]
    }
  }
}

Step 6: Testing Your Setup

The mcp_server_git repository includes test fixtures to validate functionality. Run the tests as follows:

pytest tests

This ensures that all tools and endpoints are working as expected.

Step 7: Clean Up

To stop and remove the Docker container:

docker stop mcp-git
docker rm mcp-git

Conclusion

The mcp_server_git provides a simple yet powerful way to automate Git repository interactions using Model Context Protocol. Whether you’re staging changes, creating branches, or reviewing commit logs, mcp_server_git has tools to make your workflow seamless. By using Docker, you can get started in minutes and integrate it into your development environment with ease.

Try it out today and experience a smarter way to manage your Git repositories! If you encounter any issues, don’t hesitate to debug using the provided tools or consult the official documentation.

Happy coding!