MCP Bitbucket Python MCP Server: Bitbucket Integration for AI Applications

Overview: What is MCP Bitbucket Python MCP Server?

MCP Bitbucket Python is a specialized Python implementation of an MCP (Model Context Protocol) server designed to integrate AI applications with Bitbucket repositories and related tools. This server runs locally on the same machine as your AI application, providing secure access to essential Bitbucket functionalities for seamless development processes.

🔧 Core Features & MCP Capabilities

The core functionality of the MCP Bitbucket Python server revolves around providing a standardized way for AI applications to interact with Bitbucket's rich set of repository management capabilities. Key features include:

• bb_create_repository: Create new repositories within your Bitbucket workspace.

  • Required: name
  • Optional: description, workspace (defaults to kallows), project_key, is_private, has_issues.

• bb_create_branch: Easily create branches in existing repositories.

  • Required: repo_slug, branch
  • Optional: workspace (defaults to kallows), start_point (defaults to main).

• bb_delete_repository: Remove a repository from Bitbucket when no longer needed.

  • Required: repo_slug.
  • Optional: workspace (defaults to kallows).

• bb_read_file: Retrieve file contents directly from your repository.

  • Required: repo_slug, path
  • Optional: workspace (defaults to kallows), branch (defaults to main/master).

• bb_write_file: Edit or create files within repositories.

  • Required: repo_slug, path, content.
  • Optional: workspace (defaults to kallows), branch (defaults to main), message (commit message).

• bb_create_issue: Create issues in tracked repositories for better issue management.

  • Required: repo_slug, title, content.
  • Optional: workspace (defaults to kallows), kind, priority.

• bb_delete_issue: Clean up old or unnecessary issues by deleting them.

  • Required: repo_slug, issue_id.
  • Optional: workspace (defaults to kallows).

• bb_search_repositories: Perform searches acrossBitbucket repositories using a flexible query syntax.

  • Required: query.
  • Optional: workspace (defaults to kallows), page (default: 1), pagelen (default: 10, max: 100).

• bb_delete_file: Remove files from repositories as required.

  • Required: repo_slug, path.
  • Optional: workspace (defaults to kallows), branch (defaults to main), message (commit message).

• bb_create_pull_request: Automatically create pull requests for easier code review and merging.

  • Required: repo_slug, title, source_branch.
  • Optional: workspace (defaults to kallows), destination_branch (defaults to main), description, close_source_branch (default: true).

⚙️ MCP Architecture & Protocol Implementation

The MCP Bitbucket Python server operates by adhering strictly to the Model Context Protocol, ensuring compatibility and seamless communication between various AI applications and backend services. The core architecture involves:

• MCP Client Interface: An intermediary that connects your AI application directly with the MCP server.
• Bitbucket API Layer: This layer handles interactions with the Bitbucket API using predefined functions provided by the bitbucket_api module.

The protocol flow is designed to maintain security and efficiency, ensuring that only authorized actions are performed on Bitbucket repositories. The following Mermaid diagram illustrates this interaction:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Bitbucket API]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

🚀 Getting Started with Installation

To set up the MCP Bitbucket Python server on your system, follow these steps:

# Clone the repository
git clone https://github.com/kallows/mcp-bitbucket.git

Once cloned, install the necessary dependencies by ensuring you have npx and any required libraries installed. The project also includes a pyproject.toml file for package management.

💡 Key Use Cases in AI Workflows

Real-time Code Collaboration

Imagine an AI application where developers can create repositories on the fly as projects are initiated. They then push code changes and collaborate through Git branches managed by the MCP Bitbucket Python server, ensuring that every bit of source code is neatly tracked and accessible.

Automated Issue Tracking

Another scenario involves automated issue tracking in Bitbucket using pull requests for feature proposals or bug fixes. When an issue is identified, the MCP client can immediately notify相关部门或团队，并自动创建一个相关的问题，简化了问题报告和解决的流程。

🔌 Integration with MCP Clients

MCP Bitbucket Python server ensures compatibility and seamless integration with popular AI applications like Claude Desktop, Continue, Cursor, etc., as demonstrated in our compatibility matrix below:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

📊 Performance & Compatibility Matrix

The performance of the MCP Bitbucket Python server has been meticulously validated to ensure seamless and efficient operation. The compatibility matrix clearly illustrates the level of support for various tools, resources, and prompts across different MCP clients:

Tool	Status
Repository Creation	✅
Branch Management	✅
Issue Tracking	✅
Pull Request Handling	✅

🛠️ Advanced Configuration & Security

To configure the server, you need to set up Bitbucket credentials as environment variables:

export BITBUCKET_USERNAME="your-username"
export BITBUCKET_APP_PASSWORD="your-app-password"

Additionally, for advanced configurations or customizations, refer to the src/bitbucket_api/server.py file. A sample configuration snippet is provided below:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

❓ Frequently Asked Questions (FAQ)

1. How do I ensure secure communication between the MCP client and the server?

   • The server uses standard HTTPS protocols for secure API interactions, ensuring that data is transferred securely.

2. Can I use this service with other Git providers besides Bitbucket?

   • Currently, the implementation focuses specifically on Bitbucket. However, similar APIs exist for other services; integrating them would be a straightforward process.

3. Is it possible to automate issue creation and tracking within my projects?

   • Yes, the server supports automated issue creation and tracking through pull requests, making workflows more efficient.

4. How can I manage configuration settings globally?

   • Configuration options are stored in JSON files within the source directory; changes here will apply system-wide.

5. What are the best practices for using this MCP server in a distributed team environment?

   • Implementing role-based access control and proper versioning policies ensures that all users have the correct permissions to interact with repositories and tools.

👨‍💻 Development & Contribution Guidelines

If you're interested in contributing to MCP Bitbucket Python or want to explore its inner workings, our development guide is a great place to start. It covers everything from setting up a local development environment to writing tests and submitting pull requests.

1. Clone the repository: git clone https://github.com/kallows/mcp-bitbucket.git
2. Set up the environment:

   pip install -r requirements.txt
   

3. Run tests:

   pytest
   

🌐 MCP Ecosystem & Resources

Explore the broader MCP ecosystem by visiting the official Model Context Protocol documentation and community forums. There, you'll find additional resources, tools, and real-world examples to support your integration efforts.

• Model Context Protocol Documentation
• Community Forum

By leveraging this comprehensive MCP Bitbucket Python server, you can enhance the capabilities of AI applications, making them more integrated with essential development tools like Bitbucket. This setup not only streamlines workflows but also paves the way for seamless collaboration and efficient project management in modern, tech-driven environments.