AI Model Detector MCP Server

Real-time VS Code AI model detection MCP server with SQLite3 storage analysis, per-chat context awareness, and 100% accuracy.

🎯 Features

• Panel-Only Detection: Reads the authoritative chat.currentLanguageModel.panel key from VS Code's storage database
• Real-time Results: Instantly surfaces the active model ID, vendor, and family with high confidence
• Zero Fallbacks: No heuristics or settings parsing—every answer is sourced from the panel configuration
• SQLite3 Storage Analysis: Direct queries against state.vscdb ensure reproducible evidence
• Model Intelligence: Bundled model metadata enables capability lookups and validation workflows
• MCP Protocol: Standard Model Context Protocol interface for easy integration

📦 Installation

NPM (Global)

npm install -g @emblem-projects/ai-model-detector-mcp

NPX (No Installation)

npx @emblem-projects/ai-model-detector-mcp

Claude Desktop Configuration

Add to your Claude Desktop claude_desktop_config.json:

{
  "mcpServers": {
    "ai-model-detector": {
      "command": "npx",
      "args": [
        "-y",
        "@emblem-projects/ai-model-detector-mcp"
      ]
    }
  }
}

Or use the local path (if installed globally):

{
  "mcpServers": {
    "ai-model-detector": {
      "command": "node",
      "args": [
        "/path/to/node_modules/@emblem-projects/ai-model-detector-mcp/dist/index.js"
      ]
    }
  }
}

Other MCP Clients

For VS Code with MCP support, add to mcp.json:

{
  "servers": {
    "ai-model-detector": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@emblem-projects/ai-model-detector-mcp"
      ]
    }
  }
}

🛠️ Available Tools

1. detect_current_model

Detects the currently active AI model by reading VS Code's panel configuration.

Response includes:

• Model ID (e.g., copilot/gpt-5-codex)
• Model family (Claude, GPT, Gemini, etc.)
• Vendor (Anthropic, OpenAI, Google, etc.)
• Detection source (storage:panel)
• Confidence score

Example:

{
  "model": "copilot/gpt-5-codex",
  "family": "gpt",
  "vendor": "OpenAI",
  "source": "storage:panel",
  "confidence": "high"
}

2. validate_model_access

Validates the panel detection pipeline and optionally returns the catalog of known models.

Response includes:

• Detection capability status
• Optional list of supported model definitions
• VS Code storage status summary

3. get_model_capabilities

Analyzes capabilities of a known AI model.

Parameters:

• modelId: Model identifier to analyze

Response includes:

• Model family classification
• Vendor information
• Typical capabilities (context window, features)

4. monitor_model_changes

Starts monitoring for model changes.

Parameters:

• interval: Monitoring interval in milliseconds (default: 5000)

🔧 Requirements

• Node.js: >= 18.0.0
• VS Code: >= 1.85.0 (for detection to work)
• Operating System: Windows, macOS, or Linux

📊 Detection Method

The MCP server performs a single, deterministic lookup:

1. Panel Storage Query: Reads chat.currentLanguageModel.panel from VS Code's state.vscdb

• Source: storage:panel
• Confidence: high (direct panel configuration)
• Failure Mode: Returns an explicit error if the key is missing

No editor, editing-session, or settings.json fallbacks are executed—every answer is grounded in the panel selection.

🚀 Usage Examples

With Claude Desktop

Once configured, ask Claude:

• "What model are we currently using?"
• "Detect the current AI model"
• "What models are available?"

Claude will use the MCP tools to provide accurate, real-time information.

Programmatic Access

import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js';

const transport = new StdioClientTransport({
  command: 'node',
  args: ['node_modules/@emblem-projects/ai-model-detector-mcp/dist/index.js']
});

const client = new Client({
  name: 'my-app',
  version: '1.0.0'
}, {
  capabilities: {}
});

await client.connect(transport);

// Detect current model
const result = await client.callTool({
  name: 'detect_current_model',
  arguments: {}
});

console.log(result);

📝 Development

Building from Source

git clone https://github.com/emblem-projects/ai-model-detector-mcp.git
cd ai-model-detector-mcp
npm install
npm run build

Testing

# Run test detection
node test-storage-detection.js

# Test MCP server
node start.mjs

🔒 Privacy & Security

• Local Only: All detection happens locally on your machine
• No External Calls: No data is sent to external services
• Read-Only: Only reads VS Code storage, never writes
• SQLite3 Safety: Uses proper SQLite3 binding with error handling

📄 Version History

2.1.0 (Current)

• Refactored to use RealVSCodeBridge for accurate detection
• Enhanced per-chat context awareness
• Improved error handling and logging
• Added comprehensive test suite

2.0.0

• Initial MCP server implementation
• Basic model detection via storage analysis

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📧 Support

• Issues: GitHub Issues
• Email: [email protected]

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built on the Model Context Protocol by Anthropic
• Uses @modelcontextprotocol/sdk
• Inspired by the need for accurate AI model detection in VS Code

Made with ❤️ by Emblem Projects