Contentful MCP Server Integration

What does “Contentful” MCP Server do?

The Contentful MCP (Model Context Protocol) Server acts as a bridge between AI assistants and the Contentful Management API, allowing seamless access to content management capabilities from within AI-driven workflows. By exposing the Contentful API through the MCP protocol, this server empowers developers to integrate advanced content operations—such as querying, creating, updating, and managing content models—directly from AI assistants. This enhances productivity by enabling tasks like content structure introspection, entry manipulation, and workflow automation, all without leaving the development environment. The Contentful MCP Server is particularly useful for teams leveraging Contentful as a headless CMS, as it simplifies and standardizes how AI agents interact with content data, facilitating rapid prototyping, automated migrations, and streamlined editorial processes.

List of Prompts

No information available about prompt templates in the repository.

List of Resources

No information available about resources provided by the Contentful MCP Server in the repository.

List of Tools

No explicit list of tools (e.g., query_database, read_write_file, call_api) found directly in the available files or documentation.

Use Cases of this MCP Server

• Content Model Introspection: Developers can programmatically fetch and analyze Contentful content model structures, making it easier to understand and document the content schema.
• Automated Content Entry Management: AI assistants can create, update, or delete entries in Contentful, streamlining editorial workflows and reducing manual content operations.
• Migration & Synchronization Workflows: Automate the migration of content or changes between Contentful environments (e.g., staging to production) using AI-driven scripts.
• Content Validation & Quality Assurance: Enable AI to review and validate content entries for completeness, consistency, or adherence to editorial guidelines before publishing.
• Integration with Deployment Pipelines: Facilitate content updates or schema changes as part of CI/CD processes, allowing AI agents to ensure content readiness alongside code deployments.

How to set it up

Windsurf

1. Ensure Node.js is installed.
2. Locate your Windsurf configuration file.
3. Add the Contentful MCP Server to the mcpServers object as shown below.
4. Save the configuration and restart Windsurf.
5. Verify the server is running and accessible.

{
  "mcpServers": {
    "contentful-mcp": {
      "command": "npx",
      "args": ["@contentful/mcp-server@latest"],
      "env": {
        "CONTENTFUL_MANAGEMENT_TOKEN": "${CONTENTFUL_MANAGEMENT_TOKEN}"
      }
    }
  }
}

Secure your Contentful Management API Key using environment variables as shown above.

Claude

1. Install Node.js if it’s not already present.
2. Open the Claude configuration file.
3. Insert the following snippet to add the Contentful MCP Server.
4. Save and restart the Claude environment.
5. Confirm connectivity to the Contentful MCP Server.

{
  "mcpServers": {
    "contentful-mcp": {
      "command": "npx",
      "args": ["@contentful/mcp-server@latest"],
      "env": {
        "CONTENTFUL_MANAGEMENT_TOKEN": "${CONTENTFUL_MANAGEMENT_TOKEN}"
      }
    }
  }
}

API keys should be set using environment variables for security.

Cursor

1. Make sure Node.js is installed.
2. Edit the Cursor configuration file.
3. Register the Contentful MCP Server as per the following example.
4. Save changes and restart Cursor.
5. Test the integration.

{
  "mcpServers": {
    "contentful-mcp": {
      "command": "npx",
      "args": ["@contentful/mcp-server@latest"],
      "env": {
        "CONTENTFUL_MANAGEMENT_TOKEN": "${CONTENTFUL_MANAGEMENT_TOKEN}"
      }
    }
  }
}

Always store sensitive keys like Contentful Management Token in environment variables.

Cline

1. Install Node.js (if not already installed).
2. Locate the Cline configuration file.
3. Add the MCP Server configuration as below.
4. Save the file and restart Cline.
5. Validate that the server is up and running.

{
  "mcpServers": {
    "contentful-mcp": {
      "command": "npx",
      "args": ["@contentful/mcp-server@latest"],
      "env": {
        "CONTENTFUL_MANAGEMENT_TOKEN": "${CONTENTFUL_MANAGEMENT_TOKEN}"
      }
    }
  }
}

Utilize environment variables to secure API credentials.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "contentful-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “contentful-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

A solid MCP implementation for Contentful management, but the lack of publicly documented tools, prompts, and resources limits its flexibility for developers. Security practices are good, and the setup is well described. Overall, it’s a promising project for Contentful users but would benefit from more thorough documentation of MCP primitives.

MCP Score