PubNub MCP Server

What does “PubNub” MCP Server do?

The PubNub MCP (Model Context Protocol) Server is designed to bridge AI assistants and development environments with real-time communication capabilities and external data sources. By leveraging PubNub’s reliable messaging infrastructure, this MCP server enables seamless integration with APIs, databases, and various resources, thus enriching the development workflow. It facilitates tasks such as subscribing to messaging channels, managing files, triggering API calls, and providing real-time data streams, all through a standardized protocol. The PubNub MCP Server is compatible with platforms like Cursor, Windsurf, Claude Desktop, Claude Code, and OpenAI Codex, making it easier for developers to enhance coding tasks, debugging, and collaboration by connecting their tools to live, contextual data and actions.

List of Prompts

No prompt templates were found in the repository or documentation.

List of Resources

No explicit resources were listed in the repository files or documentation.

List of Tools

No explicit list of tools could be confirmed from the available files (e.g., server.py or equivalent tool-defining files are not present in the repository).

Use Cases of this MCP Server

• Real-Time Messaging Integration
  Connect your development environment or AI agent to PubNub channels, enabling real-time communication and collaboration between team members or bots.

• API Interaction
  Facilitate automated API calls or data fetching through the MCP server, allowing AI assistants to trigger PubNub-powered workflows seamlessly within supported editors.

• Contextual Data Streaming
  Stream contextual data (such as code changes, notifications, or alerts) into your IDE or AI assistant, enhancing situational awareness for developers.

• Collaboration in Coding Platforms
  Use the PubNub MCP Server to support live code reviews, instant feedback, and shared sessions across tools like Cursor or Claude Code.

How to set it up

Windsurf

1. Ensure Node.js is installed on your system.
2. Install the package:

   npm install -g @pubnub/mcp-server@latest
   

3. Edit or create the Windsurf configuration file.
4. Add the PubNub MCP Server using a JSON snippet in the mcpServers section:

   {
     "mcpServers": {
       "pubnub-mcp": {
         "command": "pubnub-mcp-server",
         "args": []
       }
     }
   }
   

5. Save the configuration and restart Windsurf.
6. Verify the server appears in the MCP integrations list.

Securing API Keys

Use environment variables for sensitive information:

{
  "mcpServers": {
    "pubnub-mcp": {
      "command": "pubnub-mcp-server",
      "env": {
        "PUBNUB_API_KEY": "your-api-key"
      },
      "inputs": {
        "apiKey": "${PUBNUB_API_KEY}"
      }
    }
  }
}

Claude

1. Install Node.js.
2. Install the PubNub MCP Server package:

   npm install -g @pubnub/mcp-server@latest
   

3. Open Claude’s configuration file.
4. Add the server configuration:

   {
     "mcpServers": {
       "pubnub-mcp": {
         "command": "pubnub-mcp-server"
       }
     }
   }
   

5. Restart Claude and check for the MCP server in the integrations.

Cursor

1. Ensure Node.js is available.
2. Install the MCP server:

   npm install -g @pubnub/mcp-server@latest
   

3. Modify the Cursor mcpServers configuration:

   {
     "mcpServers": {
       "pubnub-mcp": {
         "command": "pubnub-mcp-server",
         "args": []
       }
     }
   }
   

4. Save and restart Cursor.

Cline

1. Install Node.js.
2. Install the PubNub MCP Server globally:

   npm install -g @pubnub/mcp-server@latest
   

3. Add the MCP server configuration to Cline:

   {
     "mcpServers": {
       "pubnub-mcp": {
         "command": "pubnub-mcp-server"
       }
     }
   }
   

4. Restart Cline and verify the MCP integration.

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:

{
  "pubnub-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 “pubnub-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the information found, the PubNub MCP Server provides a basic MCP server implementation but lacks detailed documentation on prompts, resources, and tools. It provides generic setup instructions and supports secure API key management, but doesn’t confirm advanced MCP features like roots or sampling.

Our opinion

The PubNub MCP Server repository is functional for connecting PubNub with MCP-compatible clients, and it provides essential setup instructions. However, the lack of detailed documentation on prompt templates, resources, and explicitly exposed tools limits its out-of-the-box usability for advanced workflows. Its open-source status and cross-platform support are positives, but the absence of sampling, roots, and rich documentation means it is best suited for users already familiar with PubNub or MCP.

MCP Score