Which Tools Support MCP, and How Well

The Model Context Protocol (MCP) is rapidly becoming the standard for connecting large language models to external data sources and tools. As developers look for ways to give their AI assistants more agency, knowing which tools support MCP and how well they implement the protocol is crucial for building a productive workflow. This guide explores the current landscape of MCP adoption, from traditional code editors to specialized AI agent environments and infinite canvas workspaces.

Understanding the Landscape of MCP Support

The adoption of MCP is happening across three distinct categories of software. Each category approaches the protocol differently based on its primary user interface and how it handles local versus cloud resources.

First, we have traditional IDEs and code editors. These tools usually implement MCP to allow the AI to read local files or query external databases directly from the editor sidebar. Second, there are command line interfaces (CLIs) and standalone agents. These often act as MCP clients that can bridge the gap between your terminal and various data servers. Third, we have comprehensive workspaces that provide a visual environment for multiple tools to interact at once.

The effectiveness of an MCP implementation depends on how it handles server configuration, whether it supports both local and remote servers, and how it manages the permissions required for the AI to execute tools safely.

IDEs and Text Editors with MCP Integration

The most common place developers encounter MCP is within their primary code editor. This integration allows the LLM to access specialized context without the developer needing to manually copy and paste documentation or database schemas.

Claude Desktop

As the reference implementation for the protocol, Claude Desktop offers robust support for MCP. It allows users to configure servers via a JSON file. Once configured, the Claude interface can call tools and access resources provided by those servers. It is excellent for testing new MCP servers because it follows the specification strictly. However, it is primarily a chat interface and lacks the deep file system integration and terminal capabilities of a dedicated development environment.

Cursor and VS Code Extensions

Several extensions for Visual Studio Code are beginning to incorporate MCP capabilities. Cursor, a fork of VS Code, has been at the forefront of AI integration. In these environments, MCP serves as a bridge to external documentation or specific API definitions that the model might not have in its training data. The challenge in these tools is often managing the lifecycle of the MCP server processes alongside the editor itself.

AI Agents and Command Line Tools

CLI agents represent a different approach to MCP support. These tools often run in the terminal and use MCP to fetch context needed for specific tasks, such as debugging or generating boilerplate code.

Claude Code and Codex CLI

Tools like Claude Code operate as high agency agents. They can use MCP to query internal tools or search through complex data sets. These agents benefit from the protocol because it standardizes how they discover what they can do. Instead of writing custom logic for every integration, the agent simply queries the MCP server for a list of available tools.

You can run these agents side by side in a unified environment. For example, using coding agents in a multi panel setup allows you to see how different agents interact with the same MCP servers. This transparency is vital for debugging complex workflows where an agent might be misinterpreting the data provided by an MCP resource.

Modern Workspaces and Infinite Canvases

A newer category of tool is the infinite canvas workspace, which provides a more flexible way to interact with MCP. Unlike a traditional editor that limits you to a single window or a sidebar, these workspaces allow you to spread out your tools and see the relationship between your code, your terminal, and your AI assistant.

Deska and MCP Integration

Deska is a free desktop app for Mac, Windows, and Linux that provides an infinite canvas for development. It supports running multiple terminals and AI agents as panels. In this environment, MCP acts as the glue between different components of your project.

Because Deska is local-first, it is particularly well suited for running MCP servers that interact with your local file system or private databases. Your code and sessions stay on your machine, ensuring that the context provided via MCP remains secure. Developers can use the canvas to place an MCP server log next to the AI agent using it, making it easy to monitor exactly what information is being exchanged.

The workspace also includes a voice and chat assistant known as Ask Deska. This assistant can drive the workspace, opening panels or running commands based on the context it receives. When combined with MCP, this creates a powerful system where the AI can not only write code but also manipulate the environment based on real time data from your connected servers.

Comparison of Support Levels

When evaluating which tools support MCP, consider the following factors:

• Server Configuration: How easy is it to add and manage new MCP servers?
• Tool Execution: Does the tool allow the AI to execute functions, or just read data?
• Visibility: Can you see the communication between the agent and the MCP server?
• Environment: Is the tool a single purpose chat box or a full development workspace?

Security and Local-First Considerations

A significant part of the MCP philosophy is the ability to keep data local. Many MCP servers are designed to run on your own hardware, querying your own databases or file systems. Tools that respect a local-first approach are inherently more compatible with the privacy goals of the protocol.

When you use Ask Deska or other agents within a secure workspace, you maintain control over your API keys and your data. For those concerned about privacy, the ability to pair devices directly via a mobile app without exposing ports to the internet is a key feature. This allows you to monitor your MCP powered agents from your phone through a secure relay while the actual processing remains on your local machine.

Frequently Asked Questions

Which tools support MCP for local development?

Most tools that support MCP, including Claude Desktop and Deska, allow for local server connections. You typically configure these by pointing the application to a local executable or a Node.js script. This is ideal for querying local SQLite databases or your current project directory.

Can I use MCP with Claude Code in Deska?

Yes, you can run Claude Code as a panel within the Deska workspace. This allows the agent to utilize any MCP servers configured in your environment while you monitor the output in real time on the infinite canvas. You can manage these configurations within the settings of your workspace.

Do I need a subscription to use MCP tools?

The protocol itself is an open standard and free to use. Many tools, including the Deska desktop app, are free to download and use with your own API keys. Some platforms offer managed inference as a paid service, but the core functionality of connecting to MCP servers is usually available to all users.

Getting Started with MCP in Your Workflow

To begin using MCP, the first step is to choose a client that fits your current needs. If you are looking for a comprehensive environment where you can run multiple agents and view your code in a flexible way, a workspace like Deska is a strong option. You can place your notes next to your code editor and your AI panels to create a customized command center.

The ability to see everything at once on an infinite canvas changes how you interact with AI. Instead of switching tabs, you zoom out to see the big picture of your architecture and the data flowing through your MCP servers.

Download the app for your platform at /download and start building your own MCP powered development environment.