Campertunity MCP Server Integration

What does “Campertunity” MCP Server do?

The Campertunity MCP (Model Context Protocol) Server is a specialized tool that connects AI assistants with comprehensive camping and outdoor recreation data. By implementing the MCP standard, it enables AI models and developer workflows to query for camping places, retrieve detailed campground information, check site availability, and facilitate bookings directly through standardized tool APIs. This integration empowers developers to automate or enhance tasks like searching for campgrounds with specific amenities, verifying real-time availability, and managing campground bookings, all while leveraging up-to-date data from the Campertunity platform. The server is designed to streamline outdoor trip planning and resource discovery for both end users and AI-powered applications by exposing these capabilities in a reusable, programmatic interface.

List of Prompts

No prompt templates are mentioned in the repository or documentation.

List of Resources

No explicit MCP resources are documented in the repository or README. The server provides data access via its tools, but no MCP resources are enumerated.

List of Tools

• place-search
  Search for camping places using filters such as location, dates, number of guests, tags (site types, activities, amenities, terrain), and descriptive text.

• place-details
  Retrieve detailed information about a specific camping place based on its ID.

• place-availability
  Check the availability of camping sites at a particular place for selected dates and optional site IDs.

• place-book
  Book a camping site, specifying place ID, date range, and number of adults and children.

Use Cases of this MCP Server

• Campground Search and Discovery
  Developers can integrate advanced search functionality for campgrounds, filtering by amenities, activities, terrain, and more to help users find the perfect site.

• Real-Time Availability Checking
  Integrate real-time queries to check site or campground availability, enabling dynamic trip planning and up-to-date recommendations.

• Booking Automation
  Automate the process of booking campsites for users, reducing manual steps and ensuring a seamless reservation experience.

• Personalized Trip Planning
  Use descriptive and filter-based searches to tailor recommendations based on user preferences, such as pet-friendly sites or specific activities.

• AI-Powered Outdoor Assistant
  Enable conversational agents to answer questions about camping options, site features, or to assist with the booking process directly.

How to set it up

Windsurf

1. Obtain your Campertunity API key from https://campertunity.com/mcp.
2. Install the MCP server package using npx.
3. Add the server configuration to your MCP client (e.g., in your Windsurf settings).
4. Include your API key as an environment variable.
5. Save your configuration and restart Windsurf.

Example JSON configuration

{
  "mcpServers": {
    "campground-search-mcp-server": {
      "command": "npx",
      "args": ["-y", "campertunity-mcp-server@latest"],
      "env": {
        "CAMPERTUNITY_API_KEY": "your_api_key_here"
      }
    }
  }
}

Securing API keys

Store your API key in an environment variable and reference it in your configuration as shown above.

Claude

1. Get your Campertunity API key from https://campertunity.com/mcp.
2. Install the server via npx or similar method supported by Claude.
3. Edit the configuration file to register the MCP server and set the environment variable for the API key.
4. Save your changes and restart Claude.
5. Verify that the MCP server is connected.

Example JSON configuration

{
  "mcpServers": {
    "campground-search-mcp-server": {
      "command": "npx",
      "args": ["-y", "campertunity-mcp-server@latest"],
      "env": {
        "CAMPERTUNITY_API_KEY": "your_api_key_here"
      }
    }
  }
}

Cursor

1. Obtain your Campertunity API key from https://campertunity.com/mcp.
2. Install the server using npx.
3. Add the configuration for the MCP server in Cursor’s settings.
4. Reference your API key via the env section of the configuration.
5. Restart Cursor to apply the configuration.

Example JSON configuration

{
  "mcpServers": {
    "campground-search-mcp-server": {
      "command": "npx",
      "args": ["-y", "campertunity-mcp-server@latest"],
      "env": {
        "CAMPERTUNITY_API_KEY": "your_api_key_here"
      }
    }
  }
}

Cline

1. Retrieve your API key from https://campertunity.com/mcp.
2. Use npx to install the MCP server or configure it according to Cline’s setup instructions.
3. Edit your client’s configuration file to include the MCP server and its environment.
4. Save and restart Cline.
5. Confirm the server is operational.

Example JSON configuration

{
  "mcpServers": {
    "campground-search-mcp-server": {
      "command": "npx",
      "args": ["-y", "campertunity-mcp-server@latest"],
      "env": {
        "CAMPERTUNITY_API_KEY": "your_api_key_here"
      }
    }
  }
}

Note:
Always keep your API keys secure. Use environment variables in your configuration (as shown above) to avoid hardcoding sensitive values.

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:

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

Overview

My overall rating for the Campertunity MCP server would be 5/10. It provides clear tool APIs and setup instructions but lacks prompt templates, explicit MCP resources, and details about roots or sampling support. The project is functional for its use case, but documentation could be enhanced for broader MCP capabilities.

MCP Score