MCP server

@splithub/mcp is a stdio Model Context Protocol server that exposes the splithub CLI as tools. Claude Code, Cursor, Codex, Windsurf or any MCP client can create experiments, read verdicts, start, pause, finish and push, using the same config file and credentials as the CLI.

Register it#

npx splithub init and npx splithub skills --mcp write the entries. By hand:

// .mcp.json (Claude Code, committed) · .cursor/mcp.json (Cursor)
{ "mcpServers": { "splithub": { "command": "npx", "args": ["-y", "@splithub/mcp"] } } }

claude mcp add splithub -- npx -y @splithub/mcp

Other clients: command npx, args -y @splithub/mcp.

Environment: SPLITHUB_PROJECT_DIR (defaults to the client's working directory), SPLITHUB_API (staging or a self-hosted cloud), SPLITHUB_API_KEY (CI), SPLITHUB_CLI (path to a specific CLI build).

Tools#

Tool	Does	Notes
splithub_status	every experiment with status, visitors, days, verdict headline	start here
splithub_results	full verdict for one experiment	the agent should report verdict.message verbatim
splithub_review	AI reviews stored in the cloud (Pro): at start, on verdict changes or a weekly check, on finish, on demand	an analyst's opinion; the verdict stays the source of truth
splithub_create_experiment	adds an experiment to splithub.config.ts	kind, id, variants, weights, goal, values, match/to, status, description
splithub_set_status	running / paused / finished	live in cloud mode
splithub_push / splithub_pull	sync the config with the cloud	pull adopts dashboard-created experiments
splithub_doctor	checks the installation	run after wiring code
splithub_login	returns a sign-in link and a code	the agent shows it to the user and waits
splithub_whoami	connected project and public key
splithub_init	sets up SplitHub in the project	once per project

Every tool returns the CLI's --json output as text. Errors come back with isError and, when the folder is not connected, a hint to call splithub_login.

A typical session#

1. User: "A/B test the pricing page headline."
2. Agent calls splithub_status (is anything running on that page?), then splithub_create_experiment with kind: value, two variants, the signup goal.
3. Agent edits the component to read useValue('pricing-title'), calls splithub_doctor.
4. Agent asks the user to confirm, then splithub_set_status running and splithub_push.
5. Days later: splithub_results → "keep running; the estimate is 3,200 visitors per variant in total" or "winner found". The agent ships the winner and calls splithub_set_status finished.

The agent skills tell the agent these rules; the MCP tools are what it executes them with.