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Hoverfly MCP Server is a Spring Boot app that exposes Hoverfly via the Model Context Protocol (MCP), enabling AI assistants like Cursor or Copilot to manage mock APIs automatically. It helps simulate unavailable APIs with simple JSON, supporting full mock lifecycle control.

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Hoverfly MCP Server

A Spring Boot-based Model Context Protocol (MCP) server that exposes Hoverfly as a programmable tool for AI assistants like Cursor, Claude Desktop, GitHub Copilot, or any other assistant supporting MCP. It enables dynamic mocking of third-party APIs to unblock development and testing when external services are unavailable.


🛠️ Installation

Note: The following prerequisites and build steps are only required if you want to contribute or build from source. For most users, using the Docker image is sufficient.

Prerequisites

  • Java 17
  • Maven 3.6+

Build & Run

git clone <repository-url>
cd hoverfly-mcp-server
mvn clean package
java -jar target/hoverfly-mcp-server-<version>.jar

Replace <version> with the actual version of the JAR you built.


⚙️ Configuring as an MCP Server (with Docker)

To use this server with an AI assistant that supports Model Context Protocol (MCP), add the following to your .mcp.json or mcp.config.json:

{
  "mcpServers": {
    "hoverfly-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-p 8500:8500",
        "-p 8888:8888",
        "-v /path/to/your/data:/opt/hoverfly-mcp/simulation-data",
        "docker.io/kapish88/hoverfly-mcp-server"
      ]
    }
  }
}
  • 8500: Hoverfly proxy port (mocked services)
  • 8888: Hoverfly admin port (mock control API)
  • -v /path/to/your/data:/opt/hoverfly-mcp/simulation-data: Mount a volume for simulation persistence

Make sure Docker is installed. The image will be pulled automatically if not available locally.


🔧 Exposed MCP Tools

Tool Name Description
get_hoverfly_status Checks if Hoverfly is running
start_hoverfly_web_server Starts Hoverfly in simulate mode as a web server. By default, it will auto-load the most recent simulation from /opt/hoverfly-mcp/simulation-data if available, unless auto-load is disabled.
stop_hoverfly_server Stops Hoverfly and clears mocks
fetch_hoverfly_version Returns Hoverfly version
list_hoverfly_mocks Lists all active mock APIs (request-response pairs)
add_hoverfly_mock Adds a mock API using a JSON RequestResponsePair definition
clear_hoverfly_mocks Removes all existing mock APIs
show_hoverfly_endpoints_info Returns key Hoverfly endpoints and example usage
get_hoverfly_documentation Returns Hoverfly documentation for a specific topic
suggest_hoverfly_matchers Suggests matcher options for a given request-response pair JSON
get_hoverfly_debug_logs Fetches recent Hoverfly logs for debugging (limit is optional)
download_hoverfly_simulation Downloads current simulation to /opt/hoverfly-mcp/simulation-data (persistent simulation directory)

These tools can be invoked programmatically by AI assistants through the AI Assistant host.


💾 Simulation Persistence

The server supports simulation persistence through a fixed, mounted volume:

Auto-Load on Startup

When starting Hoverfly with start_hoverfly_web_server, the server will by default:

  • Load the most recent simulation file from /opt/hoverfly-mcp/simulation-data (if available)
  • Start with a clean state if no simulation file is found

You can disable auto-load by setting the appropriate parameter.

Persistent Data and Volume Mounting

To persist simulation data across restarts, you must mount a host directory to /opt/hoverfly-mcp/simulation-data inside the container.
This is the only supported location for persistent data.

Note: The host directory must be writable by the container user.

Simulation files are only saved to this directory when you explicitly invoke the download tool.

🤝 Contributing

  1. Fork this repo
  2. Create a feature branch
  3. Submit a Pull Request

📜 License

See LICENSE file for licensing terms.

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Hoverfly MCP Server is a Spring Boot app that exposes Hoverfly via the Model Context Protocol (MCP), enabling AI assistants like Cursor or Copilot to manage mock APIs automatically. It helps simulate unavailable APIs with simple JSON, supporting full mock lifecycle control.

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