Pipe2.ai

Official5 tools

by pipe2

Run multi-step AI pipelines for video, image, audio and text: upload media, run, poll results.

ai.pipe2/mcp — Model Context Protocol (MCP) Server

ai.pipe2/mcp is an MCP server that runs multi-step AI pipelines for video, image, audio, and text. It supports a workflow where media can be uploaded, pipeline execution can be started, and results can be polled. The server is described as having 5 tools.

🛠️ Key Features

Multi-step AI pipeline execution
Media upload capability
Polling for pipeline results
Coverage includes video, image, audio, and text
5 tools

🚀 Use Cases

Running AI processing pipelines on uploaded video
Running AI processing pipelines on uploaded images
Running AI processing pipelines on uploaded audio
Running AI processing pipelines on text inputs

⚡ Developer Benefits

Consistent MCP tool set (5 tools)
Pipeline flow split into upload, run, and poll steps

Captured live from the server via tools/list.

get_pipeline_run_status

Check the status of a pipeline run. Returns status (pending/running/completed/failed), output data, error messages, timestamps, and generated asset URLs.

Parameters1

run_id

string

required

The pipeline run ID returned by run_pipeline

Raw schema

{
  "type": "object",
  "properties": {
    "run_id": {
      "description": "The pipeline run ID returned by run_pipeline",
      "type": "string"
    }
  },
  "required": [
    "run_id"
  ]
}

get_pipeline_schema

Get 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.

Parameters1

pipeline_slug

string

required

The slug identifier of the pipeline (e.g., 'image-generator', 'video-generator')

Raw schema

{
  "type": "object",
  "properties": {
    "pipeline_slug": {
      "description": "The slug identifier of the pipeline (e.g., 'image-generator', 'video-generator')",
      "type": "string"
    }
  },
  "required": [
    "pipeline_slug"
  ]
}

list_pipelines

List all available AI video/image pipelines. Returns name, slug, description, category, credit cost, and required providers for each active pipeline.

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "required": []
}

request_upload

Request 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 Supported content types: image/* (max 10MB), video/* (max 50MB), audio/* (max 20MB)

Parameters2

content_type	string	required	MIME type of the file (e.g., 'image/jpeg', 'image/png', 'video/mp4', 'audio/mpeg')
filename	string	required	Name of the file to upload (e.g., 'photo.jpg', 'video.mp4')

Raw schema

{
  "type": "object",
  "properties": {
    "content_type": {
      "description": "MIME type of the file (e.g., 'image/jpeg', 'image/png', 'video/mp4', 'audio/mpeg')",
      "type": "string"
    },
    "filename": {
      "description": "Name of the file to upload (e.g., 'photo.jpg', 'video.mp4')",
      "type": "string"
    }
  },
  "required": [
    "filename",
    "content_type"
  ]
}

run_pipeline

Run 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.

Parameters2

input	object	required	Pipeline input fields as a JSON object. Use get_pipeline_schema to see required fields for each pipeline.
pipeline_slug	string	required	The slug identifier of the pipeline to run (e.g., 'image-generator', 'video-generator')

Raw schema

{
  "type": "object",
  "properties": {
    "input": {
      "description": "Pipeline input fields as a JSON object. Use get_pipeline_schema to see required fields for each pipeline.",
      "properties": {},
      "type": "object"
    },
    "pipeline_slug": {
      "description": "The slug identifier of the pipeline to run (e.g., 'image-generator', 'video-generator')",
      "type": "string"
    }
  },
  "required": [
    "pipeline_slug",
    "input"
  ]
}

Install

Tools (5)

get_pipeline_run_statusget_pipeline_schemalist_pipelinesrequest_uploadrun_pipeline

Remote endpoint

Streamable HTTP

Hosted server - connect over the network, no local install.

url

https://mcp.pipe2.ai/mcp

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "mcp": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.pipe2.ai/mcp"
      ]
    }
  }
}

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.

ai.pipe2/mcp — Model Context Protocol (MCP) Server

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

get_pipeline_run_status

get_pipeline_schema

list_pipelines

request_upload

run_pipeline

Install

Remote endpoint

Related servers