Code Intelligence MCP

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Intelligent code suggestion MCP service that provides AI-powered component and utility method recommendations for AI IDEs.

Introduction

This is an intelligent code suggestion service based on Model Context Protocol (MCP). It analyzes user requirements through AI and recommends the most suitable components and utility methods from private code repositories, helping developers improve code reuse and development efficiency.

Core Capabilities

🎨 UI Component Intelligent Recommendation

• Analyze UI development requirements (pages, forms, interfaces, etc.)

• Match the most relevant private components from the knowledge base

• Generate optimized prompts with component imports and usage

• Provide complete implementation guides and code examples

🔧 Utility Method Intelligent Recommendation

• Analyze logic function requirements (data processing, format conversion, utility functions, etc.)

• Find reusable utility methods from the method knowledge base

• Generate optimized prompts with method imports and invocation

• Avoid reinventing the wheel and improve code quality

Related MCP server: Rules MCP Server

Features

Intelligent Analysis Engine

• Requirement Understanding: Deep understanding of user development intentions based on AI

• Keyword Extraction: Automatically identify core elements in requirements

• Complexity Assessment: Intelligently evaluate implementation difficulty and component fit

Knowledge Base Management

• Component Knowledge Base: Manage private UI component library (props, events, slots, examples)

• Method Knowledge Base: Manage utility method library (parameters, return values, types, usage)

• Relevance Algorithm: Calculate recommendation scores based on semantic matching

Prompt Optimization

• Bidirectional Optimization: Support prompt redesign for both component and method scenarios

• Structured Output: Generate complete solutions including import statements and implementation steps

• Best Practices: Integrate code standards and usage recommendations

Project Structure

code-intelligence-mcp/
├── src/
│   ├── core/                          # Core functional modules
│   │   ├── knowledge-base.ts          # Component knowledge base management
│   │   ├── utility-knowledge-base.ts  # Utility method knowledge base management
│   │   ├── prompt-redesigner.ts       # UI component prompt redesign
│   │   ├── logic-prompt-redesigner.ts # Logic method prompt redesign
│   │   ├── ai-suggester.ts            # AI component recommendation engine
│   │   ├── ai-utility-suggester.ts    # AI method recommendation engine
│   │   └── index.ts
│   ├── config/                        # Configuration management
│   │   ├── model-manager.ts           # AI model manager
│   │   ├── ai-client-adapter.ts       # AI client adapter
│   │   ├── types.ts                   # Configuration type definitions
│   │   └── index.ts
│   ├── tools/                         # MCP tool definitions
│   │   ├── suggestion.ts              # Component suggestion tool
│   │   ├── utility-suggestion.ts      # Method suggestion tool
│   │   ├── query.ts                   # Query tool
│   │   └── index.ts
│   ├── resources/                     # MCP resource definitions
│   │   └── index.ts
│   ├── types/                         # Type definitions
│   │   └── mcp-types.ts
│   ├── utils/                         # Utility functions
│   │   ├── logger.ts                  # Logger utility
│   │   ├── ai-caller.ts               # AI unified caller
│   │   ├── path-utils.ts              # Path resolution utilities
│   │   └── index.ts
│   └── mcp-server.ts                  # MCP server main entry
├── ci-mcp-data/                       # Configuration data (user-specific)
│   ├── components.example.json        # UI component knowledge base example
│   ├── utils.example.json             # Utility method knowledge base example
│   └── config.example.json            # AI model configuration example
├── scripts/                           # Script tools
│   ├── setup.js                       # Setup script
│   └── start.sh                       # Startup script
├── package.json
├── tsconfig.json
└── .npmignore                         # NPM publish exclusions

Installation and Usage

Method 1: Use with npx (Recommended)

This is the recommended way for using the MCP server with AI IDEs like Claude Desktop.

Step 1: Prepare Configuration Files

Create a configuration directory (recommended location: ~/.config/ci-mcp):

mkdir -p ~/.config/ci-mcp

Download or create the following three configuration files:

1. config.json - AI model configuration

2. components.json - UI component knowledge base

3. utils.json - Utility method knowledge base

You can find example files in the npm package or repository.

Step 2: Configure AI IDE

Add to your AI IDE configuration (e.g., Claude Desktop's claude_desktop_config.json):

{
  "mcpServers": {
    "code-intelligence": {
      "command": "npx",
      "args": ["-y", "code-intelligence-mcp"],
      "env": {
        "CI_MCP_DATA_DIR": "~/.config/ci-mcp"
      }
    }
  }
}

Environment Variables:

• CI_MCP_DATA_DIR (Recommended): Specify the configuration directory, all three files will be loaded from this directory

• CI_MCP_CONFIG: Directly specify the path to config.json

• CI_MCP_COMPONENTS: Directly specify the path to components.json

• CI_MCP_UTILS: Directly specify the path to utils.json

Path Formats Supported:

• Absolute path: /Users/xxx/.config/ci-mcp

• Home directory: ~/.config/ci-mcp or $HOME/.config/ci-mcp

• Environment variables: ${MY_CONFIG_DIR}/ci-mcp

Step 3: Restart AI IDE

Restart your AI IDE (e.g., Claude Desktop), and the MCP service will start automatically via npx.

Method 2: Local Development

Prerequisites

Configure Knowledge Base Data Files

The project requires manual configuration of the following data files in ci-mcp-data/ directory:

1. ci-mcp-data/config.json - AI model configuration (including API Key)

   cp ci-mcp-data/config.example.json ci-mcp-data/config.json
   # Edit config.json and fill in your API Key

2. ci-mcp-data/components.json - UI component knowledge base

   cp ci-mcp-data/components.example.json ci-mcp-data/components.json
   # Edit components.json based on your private component library

   • Add component information following the existing format: description, import, relativePath, etc.

3. ci-mcp-data/utils.json - Utility method knowledge base

   cp ci-mcp-data/utils.example.json ci-mcp-data/utils.json
   # Edit utils.json based on your utility method library

   • Include method information: description, import, params, returns, etc.

Note:

• config.json contains sensitive information (API Key) and is added to .gitignore, will not be committed to the repository

• components.json and utils.json need to be configured based on your actual code repository

• Refer to example files like config.example.json for configuration format

Install Dependencies

pnpm install

Development Mode

pnpm dev

Build

pnpm build

Production Mode

pnpm start:prod

MCP Tools

🎨 UI Component Suggestion Tools

1. suggest_components

Intelligently analyze UI development requirements and recommend the most suitable private components.

Use Cases:

• Create pages, forms, interfaces and other UI features

• Quick development using private component library

• Get complete implementation solutions

Input Parameters:

{
  prompt: string; // User requirement description, e.g. "Create user login page"
}

Output:

• Requirement Analysis: Keywords, component types, complexity assessment

• Suggested Components: Component list + relevance score + recommendation reason

• Optimized Prompt: Including specific component imports and usage

• Implementation Guide: Step-by-step development suggestions

Example:

// Input
{"prompt": "Generate a user information edit form"}

// Output
{
  "analysis": {
    "keywords": ["form", "edit", "user information"],
    "componentTypes": ["form", "input", "button"]
  },
  "suggestedComponents": [
    {
      "name": "das-form",
      "relevance": 0.95,
      "reason": "Most suitable for user information editing scenarios"
    }
  ],
  "redesignedPrompt": "Create using das-form component...",
  "implementationGuide": "1. Import component...\n2. Configure form fields..."
}

2. query_component

Query detailed information of a specific component.

Input Parameters:

{
  componentName: string; // Component name, e.g. "das-button"
}

Output:

• Component description, category, tags

• Props parameter list

• Events list

• Slots description

• Usage example code

• Import path

🔧 Utility Method Suggestion Tools

1. suggest_utilities

Intelligently analyze logic development requirements and recommend reusable utility methods.

Use Cases:

• Implement data processing and format conversion functions

• Need encryption, validation and other utility functions

• Avoid reinventing the wheel

Input Parameters:

{
  prompt: string; // Logic requirement description, e.g. "Need to format numbers with thousand separators"
}

Output:

• Requirement Analysis: Key function points, method types

• Suggested Methods: Method list + relevance score + recommendation reason

• Optimized Prompt: Including method imports and invocation

• Implementation Guide: Usage steps and notes

Example:

// Input
{"prompt": "Implement password encryption function"}

// Output
{
  "analysis": {
    "keywords": ["encryption", "password", "security"],
    "methodTypes": ["encryption", "security"]
  },
  "suggestedUtilities": [
    {
      "name": "encryptPassword",
      "relevance": 0.98,
      "reason": "Provides MD5/SHA256 password encryption"
    }
  ],
  "redesignedPrompt": "Use encryptPassword method...",
  "implementationGuide": "1. Import method...\n2. Call encryption..."
}

2. query_utility

Query detailed information of a specific utility method.

Input Parameters:

{
  utilityName: string; // Method name, e.g. "formatNumber"
}

Output:

• Method description, category, type

• Parameter list (parameter name, type, description)

• Return value type and description

• Usage example code

• Import path

MCP Resources

code-intelligence://component-library

Component Library Resource

Provides complete private component library information, including:

• List of all available components

• Component categories and tags

• Component capability overview

code-intelligence://utility-library

Utility Method Library Resource

Provides complete utility method library information, including:

• List of all available methods

• Method categories and functions

• Method capability overview

code-intelligence://usage-guide

Usage Guide Resource

Includes:

• MCP tools usage instructions

• Best practice recommendations

• FAQs

• Integration configuration guide

Tech Stack

Core Framework

• TypeScript - Type-safe development

• Node.js - Runtime environment

• MCP SDK (@modelcontextprotocol/sdk) - Model Context Protocol implementation

AI Integration

• Vercel AI SDK - Unified AI interface

• OpenAI - GPT series model support

• Anthropic - Claude series model support

• DeepSeek - Domestic large model support

Development Tools

• pnpm - Package manager

• tsx - TypeScript executor

• ESLint + Prettier - Code standards

• Husky - Git hooks

Development Standards

• Use TypeScript for type-safe development

• Follow ESLint and Prettier code standards

• Use Husky for Git hooks management

Configuration

1. MCP Service Configuration (mcp-config.json)

Register MCP service in AI IDE:

{
  "mcpServers": {
    "code-intelligence": {
      "command": "/bin/zsh",
      "args": ["/path/to/code-intelligence-mcp/scripts/start.sh"]
    }
  }
}

2. AI Model Configuration (data/config.json)

Configure AI models used by the recommendation engine:

{
  "defaultModel": "claude-3-7-sonnet-latest",
  "providers": [
    {
      "provider": "anthropic",
      "models": [
        {
          "model": "claude-3-7-sonnet-latest",
          "title": "Claude 3.7 Sonnet",
          "baseURL": "https://api.302.ai/v1",
          "apiKey": "your-api-key"
        }
      ]
    },
    {
      "provider": "openai",
      "models": [
        {
          "model": "gpt-4o",
          "title": "GPT-4o",
          "baseURL": "https://api.openai.com/v1",
          "apiKey": "your-api-key"
        }
      ]
    }
  ]
}

Configuration Description:

• defaultModel: Default model name to use, must exist in providers

• providers: List of supported AI providers

  • provider: Provider type (anthropic, openai, deepseek, ollama)

  • models: List of model configurations for this provider

    • model: Model name (must match defaultModel)

    • title: Model display name

    • baseURL: API endpoint address

    • apiKey: API key

Supported Providers:

• anthropic - Claude series models

• openai - GPT series models

• deepseek - DeepSeek domestic models

• ollama - Local models

3. Knowledge Base Data

Component Knowledge Base (data/components.json)

{
  "components": [
    {
      "name": "das-button",
      "description": "Button component",
      "category": "Basic component",
      "tags": ["button", "interaction"],
      "props": [...],
      "events": [...],
      "example": "..."
    }
  ]
}

Utility Method Knowledge Base (data/utils.json)

{
  "utilities": [
    {
      "name": "formatNumber",
      "description": "Format number with thousand separators",
      "category": "Formatting",
      "type": "formatter",
      "params": [...],
      "returns": {...},
      "example": "..."
    }
  ]
}

License

MIT

Contributing

We welcome contributions! Please see CONTRIBUTING.md for details on how to contribute to this project.

Changelog

See CHANGELOG.md for version history and release notes.