Intellij Community MCP Connector

Intellij Community MCP Connector

Overview

This skill enables Claude to interact with Intellij Community through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Intellij Community's Plugin API, allowing natural language control of Intellij Community operations, intelligent automation, and AI-powered assistance for Intellij Community workflows.

When to Use This Skill

• AI-driven code inspections and refactoring suggestions
• Project structure analysis and dependency management
• Run configuration generation and test execution
• Code generation with IntelliJ's PSI tree awareness
• Build tool integration (Maven/Gradle) through Claude

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Intellij Community   │
│   (Client)   │◀────│   (Kotlin/Java)  │◀────│   (Plugin API    )│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Intellij Community operations as tools Claude can invoke. The server translates natural language intentions into Plugin API calls.

Key Endpoints/Interfaces

PSI tree access, InspectionManager, RefactoringActionHandler, RunManager, ProjectManager

Implementation

// Intellij Community MCP Server Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "intellij-community-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Intellij Community resources with optional filters",
  {
    query: z.string().optional().describe("Search query or filter"),
    limit: z.number().optional().describe("Max results to return"),
  },
  async ({ query, limit }) => {
    // Call Intellij Community Plugin API
    const response = await fetch(`${BASE_URL}PSI tree access`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Intellij Community",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}PSI tree access`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Intellij Community data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}PSI tree access`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "intellij-community-mcp-connector": {
      "command": "node",
      "args": ["path/to/intellij-community-mcp-connector/index.js"],
      "env": {
        "INTELLIJ_COMMUNITY_API_KEY": "your-api-key",
        "INTELLIJ_COMMUNITY_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Intellij Community API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Intellij Community API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Analyze all deprecated API usages in this project and suggest modern replacements"

Security Considerations

• All API credentials must be stored as environment variables
• Implement input validation and sanitization for all tool parameters
• Use HTTPS for all API communications
• Follow the principle of least privilege for API token permissions
• Audit log all write operations for compliance tracking

Resources

• Intellij Community Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Part of SkillGalaxy - 10,000+ comprehensive skills for AI-assisted development.