Pipe2.ai
Run multi-step AI pipelines for video, image, audio and text: upload media, run, poll results.
Connect it
Settings → Connectors → Add custom connector
URL: https://mcp.pipe2.ai/mcpSettings → Apps & Connectors → Advanced → Developer mode
Create connector with URL: https://mcp.pipe2.ai/mcp{
"mcpServers": {
"mcp": {
"url": "https://mcp.pipe2.ai/mcp"
}
}
}claude mcp add --transport http mcp https://mcp.pipe2.ai/mcp{
"servers": {
"mcp": {
"type": "http",
"url": "https://mcp.pipe2.ai/mcp"
}
}
}5 tools, verified live
get_pipeline_run_statusread-onlyCheck the status of a pipeline run. Returns status (pending/running/completed/failed), output data, error messages, timestamps, and generated asset URLs.
get_pipeline_schemaread-onlyGet the input schema for a specific pipeline. Returns the JSON Schema describing required and optional input fields. Use this before running a pipeline to understand what inputs are needed.
list_pipelinesread-onlyList all available AI video/image pipelines. Returns name, slug, description, category, credit cost, and required providers for each active pipeline.
request_uploadRequest a presigned S3 upload URL for a file. Use this for pipeline inputs that require file URLs (e.g., images, videos, audio). **Two-step upload flow:** 1. Call this tool with filename and content_type to get a presigned upload URL and final asset URL 2. PUT the file contents to the upload_url (presigned, expires in 5 minutes) 3. Use the asset_url as the input value when running a pipeline Sup
run_pipelinecan modifyRun an AI pipeline by slug with the given input. Use list_pipelines to discover available pipelines and get_pipeline_schema to see required inputs. Returns a run ID for tracking status.
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