AutoContent API

AutoContent API is an MCP-native content generation platform that converts source material — URLs, documents, and text — into finished podcasts, explainer videos, infographics, and slide decks. Unlike manual NotebookLM workflows, AutoContent exposes the entire pipeline through a REST API and MCP server, making it callable from AI coding agents like Codex and Claude Code as well as from CI/CD runbooks and product integrations.

How It Works

The platform follows a standard async pattern. You submit a request with input resources (URLs or text) and an output type, receive a request_id, then either poll the status endpoint or configure a webhook. Completion is signaled by status: 100. The pipeline handles importing sources, transcribing media, creating audio, rendering video, generating visuals, and packaging final assets — the result comes back as a finished link.

MCP integration is the primary access path for agent-driven workflows. A single codex mcp add or cc mcp add command registers AutoContent as an MCP server, after which agents can request content in natural language. The vendor positions this as a way to "connect once, then ask Codex or Claude for podcasts, videos, infographics, slide decks, and repurposed content."

Output Formats

Each content request produces one of four output types: an audio podcast with transcript, an explainer video, a visual infographic, or a presentation slide deck. The public content library demonstrates output across diverse topics — from climate science and AI art ethics to career strategy and longevity research — though these samples represent the vendor's own generated content rather than independently reviewed output.

Who It's For

AutoContent targets developers and technical teams who need programmatic, repeatable content generation rather than one-off manual processes. Documented use cases include generating weekly audio briefings from internal changelogs and customer notes for GTM teams, plugging content generation into CI pipelines, and building NotebookLM-style research synthesis into products without depending on a NotebookLM account. The platform competes in the AI Podcast Assistant space alongside tools like Riverside (recording and editing focused) and PodLM (URL-to-podcast generation), but differentiates through its MCP-native agent integration and multi-format output beyond audio alone.

What to Watch For

The async architecture means production integrations must handle polling or webhook delivery — the API does not return finished content synchronously. All access requires a valid API key and MCP endpoint connectivity. Pricing details are not publicly documented in the available source material, which may complicate procurement evaluation for teams that need upfront cost modeling.