Bitrise MCP Server Integration

What does “Bitrise” MCP Server do?

The Bitrise MCP Server acts as a bridge between AI assistants and the Bitrise platform, allowing seamless programmatic access to Bitrise APIs for enhanced app development workflows. By connecting through the Bitrise MCP Server, AI assistants can manage apps, trigger builds, handle artifacts, and interact with other Bitrise resources securely and efficiently. This integration enables tasks such as automating build operations, managing app artifacts, and querying Bitrise data directly from AI-driven environments. The server supports API token-based authentication, ensuring secure access, and offers comprehensive documentation to facilitate usage. Overall, it empowers developers and AI agents to streamline continuous integration and delivery pipelines by leveraging Bitrise’s capabilities through natural language or automated workflows.

List of Prompts

No prompt templates are mentioned or documented in the available repository files or README.

List of Resources

No explicit resource primitives (such as context data or resource endpoints) are documented in the repository or README.

Apps

• list_apps
  Retrieves a list of applications accessible to the authenticated Bitrise account.

Additional tools may exist, but only list_apps under the “Apps” API group is documented in the README. Other API groups can be enabled, but specific tool names and functions are not listed.

Use Cases of this MCP Server

• App Management
  AI assistants can list, query, and manage mobile applications registered on Bitrise, streamlining team workflows.

• Build Operations
  Developers can trigger, monitor, and manage build processes for continuous integration and delivery directly from AI-powered tools.

• Artifact Management
  Retrieve and manage build artifacts, making it easy to access build outputs or distribute them automatically.

• Customizing Tool Exposure
  Teams can configure which Bitrise API groups are exposed, allowing for tailored toolsets that fit specific workflow needs and optimize resource usage.

How to set it up

Windsurf

No specific instructions for Windsurf are provided in the repository or README.

Claude

1. Generate a Bitrise API token from your Bitrise account settings.
2. Open Claude Desktop settings and navigate to the Developer tab.
3. Click Edit config to create or open claude_desktop_config.json.
4. Add the following configuration under mcpServers:

   {
     "mcpServers": {
       "bitrise": {
         "command": "uvx",
         "env": {
           "BITRISE_TOKEN": "<YOUR_TOKEN>"
         },
         "args": [
           "--from",
           "git+https://github.com/bitrise-io/bitrise-mcp@v1.1.0",
           "bitrise-mcp"
         ]
       }
     }
   }
   

5. Save the file and restart Claude Desktop. A hammer icon should appear if successful.

Cursor

No specific instructions for Cursor are provided in the repository or README.

Cline

No specific instructions for Cline are provided in the repository or README.

VS Code (as an example of a compatible platform)

1. Generate a Bitrise API token.
2. Open VS Code settings.json.
3. Add the following under the mcp key:

   {
     "mcp": {
       "inputs": [
         {
           "id": "bitrise-workspace-token",
           "type": "promptString",
           "description": "Bitrise workspace token",
           "password": true
         }
       ],
       "servers": {
         "bitrise": {
           "command": "uvx",
           "args": [
             "--from",
             "git+https://github.com/bitrise-io/bitrise-mcp@v1.0.1",
             "bitrise-mcp"
           ],
           "type": "stdio",
           "env": {
             "BITRISE_TOKEN": "${input:bitrise-workspace-token}"
           }
         }
       }
     }
   }
   

4. Save the file. Tools will load into Copilot Chat automatically.

Securing API Keys

Store your Bitrise API token in environment variables. Example for Claude:

"env": {
  "BITRISE_TOKEN": "<YOUR_TOKEN>"
}

For VS Code, use the inputs mechanism as shown above to avoid storing secrets in plain text.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "bitrise": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “bitrise” to “bitrise-mcp” or another desired name, and replace the URL with your own MCP server URL.

Overview

Based on the available documentation and README, the Bitrise MCP Server is well-documented for setup and security but lacks detail on prompt templates and explicit resource primitives. Tool documentation is minimal, and only one tool is listed. Platform setup covers Claude and VS Code, but not Windsurf, Cursor, or Cline. Sampling and Roots support are not mentioned.

Our opinion

This MCP server is solid for integration with Bitrise, particularly for teams already using Bitrise for CI/CD. Its documentation is clear for setup and security, and it exposes at least one essential tool. However, it lacks detailed prompts, resource listing, and explicit mention of advanced MCP features like Sampling or Roots. For a production-grade MCP, more comprehensive tool/resource documentation and prompt templates would be desirable.

MCP Score