A **Spring Boot**-based **Model Context Protocol (MCP)** server that exposes [Hoverfly](https://hoverfly.io/) as a programmable tool for AI
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. The hoverfly mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 13 defined tools rather than through you.
Everything the assistant can do here goes through one of these:
get_hoverfly_status — Checks if Hoverfly is runningstart_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 ifstop_hoverfly_server — Stops Hoverfly and clears mocksfetch_hoverfly_version — Returns Hoverfly versionlist_hoverfly_mocks — Lists all active mock APIs (request-response pairs)add_hoverfly_mock — Adds a mock API using a JSON RequestResponsePair definitionclear_hoverfly_mocks — Removes all existing mock APIsshow_hoverfly_endpoints_info — Returns key Hoverfly endpoints and example usageget_hoverfly_documentation — Returns Hoverfly documentation for a specific topicsuggest_hoverfly_matchers — Suggests matcher options for a given request-response pair JSONget_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)Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
Plenty of monitoring and observability servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Hoverfly's toolset — get_hoverfly_status, start_hoverfly_web_server, stop_hoverfly_server and 10 more — is a fair guide to whether it matches your workflow. It is maintained by kapishmalik; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| 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) |
| Prerequisites | The Prerequisites tool exposed by this server. |
Give your coding agent the full DevTools toolbox: traces, network, console, heap snapshots and Lighthouse.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.
Errors with full context — stack traces, issue triage and AI-powered root-cause analysis from Sentry's server.
Enables enhanced web research capabilities for large language models through intelligent search queuing and advanced content extraction.
Automates browser interactions and enables Large Language Models (LLMs) to interact with web pages through Playwright and Chrome DevTools Protocol
Guides tool usage by providing recommendations for MCP tools at each problem-solving stage.