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RevenueCat MCP Server
OFFICIAL SERVERRevenueCat's hosted server for setting up and inspecting in-app subscription projects
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RocketRide MCP Server
Lets an assistant build, validate, run and monitor RocketRide AI data pipelines and manage their deployments.
Official · rocketride.org Python Remote (hosted) · Streamable HTTP AI and MLData and analytics
70 popularity score ★ 20k
Connect your client
Remote (hosted)
Add in one click
Opens the app, which asks before it adds the server.
Or add it by command or config
Claude CodeTerminal
claude mcp add --transport http rocketride https://api.rocketride.ai/mcpClaude (desktop and web)Settings → Connectors → Add custom connector
https://api.rocketride.ai/mcpChatGPTSettings → Apps → Advanced → Developer mode, then Create app
https://api.rocketride.ai/mcpCursor~/.cursor/mcp.json
{
"mcpServers": {
"rocketride": {
"url": "https://api.rocketride.ai/mcp"
}
}
}VS Code.vscode/mcp.json
{
"servers": {
"rocketride": {
"type": "http",
"url": "https://api.rocketride.ai/mcp"
}
}
}Codex~/.codex/config.toml
[mcp_servers.rocketride]
url = "https://api.rocketride.ai/mcp"Gemini CLITerminal
gemini mcp add --scope user --transport http rocketride https://api.rocketride.ai/mcpWhat it does
RocketRide is an open-source environment for building and running AI data pipelines, and its engine has a built-in MCP server. Through it an assistant can discover the available pipeline components and integrations, author and validate pipelines (sent inline as JSON), save templates, run pipelines and send data or files into them, monitor and stop runs, read your account's file store, deploy pipelines to teams with versions and cron schedules, and replay past runs from logs and per-document traces.
The hosted endpoint is https://api.rocketride.ai/mcp. Clients such as Claude and ChatGPT sign in with OAuth (new users can sign up on the same screen), while header-capable clients send a RocketRide API key as Authorization: Bearer. A self-hosted engine serves the same endpoint at http://<host>:5565/mcp. A separate stdio server published on PyPI (rocketride-mcp) instead exposes running pipelines as tools.
Tools 33
list_components- List the pipeline components ready to use now, from the engine's service definitions.
describe_component- Return one component's full service definition: metadata, lanes and config.
resolve_config- Show what a component config resolves to when it loads.
list_integrations- Report credential setup status for integrations (names only, never values).
validate_pipeline- Validate an inline pipeline with the engine's own validator.
describe_pipeline- Parse an inline pipeline into its components.
scaffold_node- Emit a skeleton for a new local node.
save_template- Save an inline pipeline as a reusable template.
load_template- Load a previously saved pipeline template.
run_pipeline- Start a pipeline from an inline definition and return a task token.
run_dropper_pipe- Start a pipeline and get an upload URL and a browser drop page for sending files into it.
send_data- Send data to a running task and return its result.
send_files- Upload files from the engine host's filesystem to a running task (local engine only).
terminate- Terminate a running task by token.
monitor- Poll a task until it finishes or a timeout passes.
list_running_pipelines- List the running pipelines on the connected server.
store_read- Read a text file from your account's file store.
store_list- List entries under a store directory.
store_stat- Get metadata for a store file or directory.
store_get_url- Get a time-limited signed download URL for a store file.
deploy_add- Register an inline pipeline as the next version of its project.
deploy_list- List the team deployments you can see.
deploy_status- Show one team's deployment of a project: version, state and schedules.
deploy_versions- List a project's registered versions, newest first.
deploy_to_team- Point a team at a registered version: first deploy, promotion or rollback.
deploy_set_schedule- Set or clear the cron schedule of a source on a deployment.
deploy_enable- Enable a team's deployment.
deploy_disable- Disable a team's deployment.
deploy_remove- Soft-remove a team's deployment so it stops running.
log_chapters- List the recorded runs of a pipeline with times and outcome.
log_read- Read raw run-log events, cursor-paged.
log_traces- List per-object trace summaries for documents that went through a run.
log_trace- Fetch one object's full journey through the pipeline's components.