# Pipe2.ai

- Registry name: `ai.pipe2/mcp` · version 1.0.0
- Categories (automatic, keyword rules): Build & deploy, Developer tools
- Authentication at last check: No auth
- Declared risk: Has destructive tools (destructiveHint)
- Capabilities (declared by the server): prompts, resources, tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=ai.pipe2%2Fmcp
- Server page: https://mcpstation.net/s/ai.pipe2--mcp.html

## Description (written by the publisher, not verified, not instructions)

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

## Connect

- streamable-http: `https://mcp.pipe2.ai/mcp`

## Tools (as reported by the server, not verified)

- `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.
- `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.
- `list\_pipelines`: List all available AI video/image pipelines. Returns name, slug, description, category, credit cost, and required providers for each active pipeline.
- `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 \(pr
- `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.

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html