Cursor is a powerful AI-native coding environment that supports direct integration with Model Context Protocol (MCP) servers. This setup allows you to inject real-time, relevant context from tools like GitHub, Jira, and Asana directly into your AI workflows within Cursor. It also enables dynamic access to codebases, tasks, and system data, enhancing your developer productivity with high-fidelity contextual understanding.

This step-by-step guide will walk you through three methods for integrating Cursor with an MCP server using three examples: GitHub, Asana, and Datadog.

MCP Server Types Supported

Cursor supports the following types of MCP servers:

  • Local Stdio: Runs on your machine and streams responses via standard input/output (stdio).

  • Streamable HTTP: The server operates as an independent process that can handle multiple client connections

Step 1: Enable MCP in Cursor IDE

  1. Open the Cursor editor.

  2. Press Cmd/Ctrl + Shift + P to open the Command Palette.

  3. Search for and select "Cursor Settings".

  4. Click Tools & Integrations

  5. Click Add Custom MCP

  1. This will create or open the config file at one of the following paths:

macOS: `~/.cursor/mcp.json`
Windows: %USERPROFILE%\.cursor\mcp.json
  1. Add or modify your configuration as shown in the examples below.

Step 2: Choose Your MCP Server Setup

Cursor requires an MCP-compliant server that can respond to context queries. You have three setup options:

Option A: Run the MCP Server Locally

Ideal for development, testing, and debugging.

Instructions:

  1. Install Docker.

  2. Ensure Docker is running.

    • If you encounter issues pulling the image, try running `docker logout ghcr.io` to refresh your credentials.

  3. Create a GitHub Personal Access Token.

    • Choose permissions based on what you’re comfortable sharing with the LLM.

    • Learn more in the GitHub documentation.

Config Example:

{
  "mcpServers": {
    "github": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "GITHUB_PERSONAL_ACCESS_TOKEN",
        "ghcr.io/github/github-mcp-server"
      ],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "<YOUR_TOKEN>"
      }
    }
  }
}

Save and restart Cursor

Option B: Use a Hosted MCP Server (e.g., Asana, Atlassian)

The easiest option—no setup required. Note that you won’t have central control or visibility into interactions between Cursor and the integrated service.

Method 1: Via UI

  1. Navigate to the following deeplink in your browser

cursor://anysphere.cursor-deeplink/mcp/install?name=Asana&config=eyJ1cmwiOiJodHRwczovL21jcC5hc2FuYS5jb20vbWNwIn0=
  1. Name is application name

  2. Config is base64 encode of {"url":"https://mcp.asana.com/mcp"}

Method 2: Via Config

  1. Install Node and npm.

  2. Install the `mcp-remote` package.

Config Example:

{
  "mcpServers": {
    "asana": {
      "command": "npx",
      "args": ["mcp-remote", "https://mcp.asana.com/sse"]
    }
  }
}

Save and restart Cursor

Option C: Deploy Your Own Remote MCP Server

Recommended for teams and production use. This setup offers:

  • Full visibility and governance

  • Access control across employees and agents

You can deploy your MCP server using a platform like Natoma. Connect apps from the registry, and retrieve the configuration

 or Streamable HTTP endpoint, and paste it into Cursor IDE by navigating to the following deeplink in your browser

cursor://anysphere.cursor-deeplink/mcp/install?name=Datadog&config=eyJ1cmwiOiJodHRwczovL21jcC5uYXRvbWEuYXBwL3NzZSJ9
  1. Name is application name

  2. Config is base64 encode of {"url":"https://mcp.natoma.app/sse"}

Restart Cursor

Troubleshooting & Testing

To verify MCP integration:

  • Ask Cursor a question that requires context from recent activity.

  • Cursor will send a background request to the MCP server, which returns structured context.

  • Monitor Cursor MCP logs at

    • Press Cmd/Ctrl + Shift + P to open the Command Palette.

    • Search for and select "Developer: Show Logs..".

    • Click MCP Logs

  • Ensure your MCP server responds with valid JSON in the correct schema.

  • Use the MCP Inspector for debugging.

Wrap-Up

Integrating Cursor with an MCP server—whether self-hosted, remote, or SaaS—takes just a few minutes. This unlocks powerful contextual capabilities, allowing you to embed the full history, structure, and semantics of your dev stack into every AI interaction.

About Natoma

Natoma enables enterprises to adopt AI agents securely. The secure agent access gateway empowers organizations to unlock the full power of AI, by connecting agents to their tools and data without compromising security.

Leveraging a hosted MCP platform, Natoma provides enterprise-grade authentication, fine-grained authorization, and governance for AI agents with flexible deployment models and out-of-the-box support for 100+ pre-built MCP servers.

You may also be interested in:

Five pillars representing how to accelerate enterprise AI adoption

How to Accelerate Enterprise AI Adoption: The 5-Pillar Framework

Accelerating enterprise AI adoption requires the right foundation, not more pilots. Organizations deploying protocol-based infrastructure like Model Context Protocol (MCP) move from experimentation to production in weeks instead of quarters. This guide provides CIOs and innovation leaders with a proven 5-pillar framework for scaling AI adoption: standardized integration layer, automated governance, rapid deployment capability, organizational readiness, and measurement systems. The result: deploy AI tools in minutes instead of months while maintaining enterprise-grade security and control.

Five pillars representing how to accelerate enterprise AI adoption

How to Accelerate Enterprise AI Adoption: The 5-Pillar Framework

Accelerating enterprise AI adoption requires the right foundation, not more pilots. Organizations deploying protocol-based infrastructure like Model Context Protocol (MCP) move from experimentation to production in weeks instead of quarters. This guide provides CIOs and innovation leaders with a proven 5-pillar framework for scaling AI adoption: standardized integration layer, automated governance, rapid deployment capability, organizational readiness, and measurement systems. The result: deploy AI tools in minutes instead of months while maintaining enterprise-grade security and control.

Five pillars representing how to accelerate enterprise AI adoption

How to Accelerate Enterprise AI Adoption: The 5-Pillar Framework

Accelerating enterprise AI adoption requires the right foundation, not more pilots. Organizations deploying protocol-based infrastructure like Model Context Protocol (MCP) move from experimentation to production in weeks instead of quarters. This guide provides CIOs and innovation leaders with a proven 5-pillar framework for scaling AI adoption: standardized integration layer, automated governance, rapid deployment capability, organizational readiness, and measurement systems. The result: deploy AI tools in minutes instead of months while maintaining enterprise-grade security and control.

How AI-Data Integration Solves the Enterprise Workflow Bottleneck

AI workflow automation transforms manual data transfers into intelligent, automated processes through secure control points. Key requirements include unified data access and proper authentication infrastructure.

How AI-Data Integration Solves the Enterprise Workflow Bottleneck

AI workflow automation transforms manual data transfers into intelligent, automated processes through secure control points. Key requirements include unified data access and proper authentication infrastructure.

How AI-Data Integration Solves the Enterprise Workflow Bottleneck

AI workflow automation transforms manual data transfers into intelligent, automated processes through secure control points. Key requirements include unified data access and proper authentication infrastructure.

The Enterprise Guide to AI Data Integration

The path to successful AI data integration requires strategic thinking beyond technical implementation.

The Enterprise Guide to AI Data Integration

The path to successful AI data integration requires strategic thinking beyond technical implementation.

The Enterprise Guide to AI Data Integration

The path to successful AI data integration requires strategic thinking beyond technical implementation.