AI extensibility in VS Code

This article provides an overview of AI extensibility options in Visual Studio Code, helping you choose the right approach for your extension.

VS Code includes powerful AI features that enhance the coding experience:

  • Code completion: Offers inline code suggestions as you type
  • Agent mode: Enables AI to autonomously plan and execute development tasks with specialized tools
  • Chat: Lets developers use natural language to ask questions or make edits in codebase through chat interfaces
  • Smart actions: Use AI-enhanced actions for common development tasks, integrated throughout the editor

You can extend and customize each of these built-in capabilities to create tailored AI experiences that meet the specific needs of your users.

Why extend AI in VS Code?

Adding AI capabilities to your extension brings several benefits to your users:

  • Domain-specific knowledge in agent mode: Let agent mode access your company's data sources and services
  • Enhanced user experience: Provide intelligent assistance tailored to your extension's domain
  • Domain specialization: Create AI features specific to a programming language, framework, or domain
  • Extend chat capabilities: Add specialized tools or assistants to the chat interface for more powerful interactions
  • Improved developer productivity: Enhance common developer tasks, like debugging, code reviewing or testing, with AI capabilities

Extend the chat experience

Agent plugin

Start with an agent plugin to distribute a domain-specific chat workflow. Package skills for expertise and reusable tasks with an MCP server for data access and actions. Add a custom agent when users need an ongoing specialist role.

For interactive workflows, MCP Apps can provide buttons, forms, and other controls in chat. Provide useful tool results for clients that do not render the app.

Plugins are the recommended replacement for deprecated chat participants. They do not require a participant request handler. Use extension tools instead when a capability depends on editor APIs or live editor state.

Language model tool

Language model tools enable you to extend agent mode in VS Code with domain-specific capabilities. In agent mode, these tools are automatically invoked based on the user's chat prompt to perform specialized tasks or retrieve information from a data source or service. Users can also reference these tools explicitly in their chat prompt by #-mentioning the tool.

To implement a language model tool, use the Language Model Tools API within your VS Code extension. A language model tool can access all VS Code extension APIs and provide deep integration with the editor.

Key benefits:

  • Domain-specific capabilities as part of an autonomous coding workflow
  • Your tool implementation can use VS Code APIs since it runs in the extension host process
  • Easy distribution and deployment via the Visual Studio Marketplace

Key considerations:

  • Remote deployment requires the extension to implement the client-server communication
  • Reuse across different tools requires modular design and implementation

MCP tool

Model Context Protocol (MCP) tools provide a way to integrate external services with language models by using a standardized protocol. In agent mode, these tools are automatically invoked based on the user's chat prompt to perform specialized tasks or retrieve information from external data sources.

MCP tools run outside of VS Code, either locally on the user's machine or as a remote service. Users can add MCP tools through JSON configuration or VS Code extension can configure them programmatically. You can implement MCP tools through various language SDKs and deployment options.

As MCP tools run outside of VS Code, they do not have access to the VS Code extension APIs.

Key benefits:

  • Add domain-specific capabilities as part of an autonomous coding workflow
  • Local and remote deployment options
  • Reuse MCP servers in other MCP clients

Key considerations:

  • No access to VS Code extension APIs
  • Distribution and deployment require users to set up the MCP server

Chat participant (deprecated)

Chat participants are not supported in the Copilot harness. The API remains available for Local harness compatibility, but new integrations should use plugins or tools. See Replace chat participants, including options for interactive UI and custom orchestration.

Build your own AI-powered features

VS Code gives you direct programmatic access to AI models for creating custom AI-powered features in your extensions. This approach enables you to build editor-specific interactions that use AI capabilities without relying on the chat interface.

To use language models directly, use the Language Model API within your VS Code extension. You can incorporate these AI capabilities into any extension feature, such as code actions, hover providers, custom views, and more.

Key benefits:

  • Integrate AI capabilities into existing extension features or build new ones
  • Running in the extension host process allows access to VS Code extension APIs
  • Easy distribution and deployment via the Visual Studio Marketplace

Key considerations:

  • Reuse across different experiences requires modular design and implementation

Decide which option to use

When choosing the right approach for extending AI in your VS Code extension, consider the following guidelines:

  1. Choose an agent plugin when:

    • You want to distribute domain expertise and workflows with their MCP integrations
    • You are replacing a chat participant with skills, tools, and optional MCP Apps
    • Your integration should work across clients rather than depend on editor APIs

  2. Choose Language Model Tool when:

    • You want to extend chat in VS Code with specialized capabilities
    • You want automatic invocation based on user intent in agent mode
    • You want access to VS Code APIs for deep integration in VS Code
    • You want to distribute your tool through the VS Code Marketplace

  3. Choose MCP Tool when:

    • You want to extend chat in VS Code with specialized capabilities
    • You want automatic invocation based on user intent in agent mode
    • You don't need to integrate with VS Code APIs
    • Your tool needs to work across different environments (not just VS Code)
    • Your tool should run remotely or locally

  4. Choose Language Model API when:

    • You want to integrate AI capabilities into existing extension features
    • You're building UI experiences outside the chat interface
    • You need direct programmatic control over AI model requests

Next steps

Choose the approach that best fits your extension's goals:

Sample projects

10/21/2026