MCP for IntelliJ IDEs

Tools has transformed how developers interact with their integrated development environments (IDEs). One such advancement is the Model Context Protocol (MCP), a protocol designed to bridge the gap between large language models (LLMs) and external data sources, including popular IDEs like IntelliJ, PyCharm, and WebStorm. By integrating MCP into JetBrains IDEs, developers can leverage AI capabilities to enhance their workflows, access real-time data, and automate tasks directly within their development environment.

In this guide, we will explore MCP for IntelliJ IDEs, focusing on the official plugin available on the JetBrains Marketplace and the GitHub repository. We will cover everything from setup to advanced usage, ensuring you can fully utilize this powerful integration.

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What is MCP?

The Model Context Protocol (MCP) is an open-source protocol developed by Anthropic that enables AI systems, such as Claude, to securely connect with various external data sources. These sources can include files, documents, databases, and even IDEs like IntelliJ. MCP operates on a client-server architecture, where the MCP server exposes data, tools, and prompts to AI clients, allowing them to access real-time information and perform actions within a secure framework.

Why does this matter? In development, MCP empowers AI assistants to interact directly with your codebase, project files, and version control systems. Consequently, developers can benefit from AI-driven code suggestions, automated testing, and real-time debugging assistance. Moreover, MCP’s design prioritizes security, ensuring that AI systems only access data or execute commands explicitly permitted by the server configuration.

MCP for IntelliJ IDEs

The MCP Server Plugin for IntelliJ-based IDEs revolutionizes how developers integrate AI into their workflows. This plugin embeds the MCP server directly into your IDE, enabling seamless communication between LLMs and your development environment. Whether you use IntelliJ IDEA, PyCharm, or WebStorm, the plugin provides a standardized way for AI systems to access your project’s context, files, and tools.

Here are some standout features:

Secure Data Access: MCP restricts AI interactions to authorized data and actions, maintaining a secure environment.
Tool Integration: The plugin exposes tools that AI clients can use, such as reading files, running commands, or querying project metadata.
Extensibility: Developers can craft custom tools and extensions, tailoring MCP to their unique needs.

By adopting MCP in your IDE, you actively unlock AI-driven automation, smarter code suggestions, and efficient debugging—all within your familiar JetBrains environment.

Setting Up MCP with IntelliJ IDEs

Getting started with MCP in IntelliJ IDEs is straightforward. Follow these steps to set up the plugin and configure the server:

1. Install the MCP Server Plugin

Open your JetBrains IDE (e.g., IntelliJ IDEA).
Navigate to Settings > Plugins.
Search for "MCP Server" in the Marketplace.
Click Install and restart your IDE.

Alternatively, download the plugin from the JetBrains Marketplace.

2. Configure the MCP Server

After installation, configure the MCP server to communicate with your AI client (e.g., Claude Desktop). The server acts as a bridge, relaying requests from the AI to your IDE.

Access the MCP Server settings in your IDE.
Verify that the server runs on the default port (e.g., 6365). Adjust this if needed.
For multiple IDEs, set the IDE_PORT environment variable to specify the IDE’s built-in web server port.

3. Connect to the MCP Server

To link an AI client to the MCP server, adjust the client’s configuration. For instance, with Claude Desktop, update your claude_desktop_config.json:

{
  "mcpServers": {
    "jetbrains": {
      "command": "npx",
      "args": ["-y", "@jetbrains/mcp-proxy"]
    }
  }
}

This setup instructs the AI client to connect to your IDE’s MCP server via the proxy.

Using MCP with IntelliJ IDEs

With the MCP server running, you can enhance your development workflow in several ways.

Here’s how MCP integrates into daily tasks:

1. AI-Powered Code Suggestions

MCP allows AI clients to access your project’s full context—open files, dependencies, and version control history. As a result, the AI delivers highly relevant code suggestions tailored to your current work.

2. Automated Testing and Debugging

Use MCP to execute tests or debug code directly through the AI client. For example, you can request the AI to run a test suite and analyze the output, saving you time and effort.

3. Real-Time Data Access

MCP enables AI systems to query real-time project data, such as code states or build outputs. This capability proves invaluable for generating reports or spotting issues early.

Consider this scenario: You’re developing a feature and need to confirm that unit tests pass. Instead of running them manually, instruct the AI via MCP to execute the tests and summarize the results—all within your IDE.

Custom Tools and Extensions

MCP’s extensibility sets it apart. Developers can create custom tools to expose additional functionality to AI clients, ranging from simple utilities to complex API integrations.

Implementing Custom Tools

The plugin offers an extension point system for registering custom MCP tools. Here’s how to implement one:

Define the Tool: Create a class implementing the McpTool interface.
Register the Tool: Add it to your plugin’s configuration with the <mcpTool> extension point.

For example:

<idea-plugin>
  <depends>com.intellij.mcpServer</depends>
  <extensions defaultExtensionNs="com.intellij.mcpServer">
    <mcpTool implementation="com.example.MyCustomTool"/>
  </extensions>
</idea-plugin>

Tool Guidelines: Use descriptive names and clear JSON argument structures for seamless integration.

With custom tools, you actively shape MCP to fit your workflow, making it a versatile addition to your toolkit.

Troubleshooting and Common Issues

While MCP setup is typically smooth, you might encounter issues. Here are common problems and their fixes:

1. Connection Issues

Problem: Requests to the MCP server fail or return 404 errors.
Solution: Ensure the IDE’s web server accepts external connections. In Settings > Build, Execution, Deployment > Debugger, enable "Can accept external connections."

2. Node.js Compatibility

Problem: The MCP proxy fails with older Node.js versions.
Solution: Upgrade to Node.js 18 or later. With nvm, create a symbolic link to npx:

which npx &>/dev/null && sudo ln -sf "$(which npx)" /usr/local/bin/npx

3. Multiple IDEs

Problem: The AI client connects to the wrong IDE when multiple are running.
Solution: Set the IDE_PORT environment variable to target the correct IDE.

These fixes ensure a stable MCP experience in IntelliJ IDEs.

Conclusion

The MCP for IntelliJ IDEs empowers developers by integrating AI directly into their workflows. Through the Model Context Protocol, you can access real-time data, automate tasks, and build custom tools—all within a secure framework. Whether you aim to boost productivity with code suggestions or streamline testing, MCP delivers.

Ready to get started? Install the MCP Server Plugin and explore its potential. Pair it with Apidog for seamless API testing and documentation—download it for free today.

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