Introducing the ZeroGPU MCP Server

If you're running an AI agent, your host model is likely doing a lot of work it shouldn't be paying frontier prices for. For high-volume, repeatable tasks you don't always need frontier reasoning to achieve great results at scale.

That's what our new MCP server is for.

The problem

Every time your agent reads a document, tags a piece of content, or pulls structured data out of unstructured text, it's spending frontier tokens on work that a smaller, purpose-built model could handle instead. That adds up in cost and in latency, and it's not what frontier models are for.

The solution? Frontier models for reasoning. ZeroGPU for everything else.

What our MCP server does

Model Context Protocol is an open standard that lets AI assistants and agents discover and call external tools through a uniform interface. With our MCP, your host model can hand off eligible work to specialized small and nano models running across ZeroGPU's edge-powered inference network. You can connect any MCP client — Claude Desktop, Claude Code, Cursor, or your own agent runtime — to reduce cost and achieve lower latency, letting your host model stay focused on the reasoning that actually needs it.

Every generative call you push to one of our models returns a usage and savings breakdown, so you can see exactly how much offloading a task to a smaller model saves you.

[VIDEO: ZeroGPU MCP Server walkthrough]

11 tools, one server

Once connected, your host model has access to:

• Summarize — condense articles, emails, transcripts, and docs
• Classify — IAB ad-tech taxonomy, zero-shot against your own labels, or multi-axis structured classification
• Extract entities — pull people, places, organizations, dates, and custom entity types out of text
• Extract structured JSON — turn unstructured text into schema-defined fields
• Redact and detect PII — scrub or flag sensitive data before it ever reaches your host model's context
• Generate follow-ups — suggested next questions on any passage
• Short chat — quick, self-contained replies that don't need multi-step reasoning

Every tool is authenticated with an API key and project ID, speaks streamable HTTP, and returns a single JSON result your client can act on immediately.

A worked example: turning a raw article into copy with IAB classification

Here's one example of what that can look like in practice. We built this small language model for adtech developers — we've reduced teams' token spend on adtech tasks by more than 50% by switching to our specialized small language models. This example uses Claude Desktop connected to the ZeroGPU MCP server to call this adtech model.

Paste a real article into Claude with one plain prompt. Instead of reading the whole article itself, Claude calls ZeroGPU's IAB classifier as a tool. The classifier returns hard signals: content categories, audience segments, and confidence scores. Claude then turns those signals into three pieces of copy in a single response: an ad brief, a newsletter blurb, and a content pitch.

No code required. Connect the server once, and from there you can pull content in from a CMS or Drive, push outputs to your channels, or branch on confidence scores before any copy gets written — all over MCP.

Getting started

1. Sign in to the ZeroGPU dashboard → create an API key and project ID.
2. Add the ZeroGPU server to your MCP client's config, or register it in Claude Code from the terminal.
3. Ask your host model to summarize, classify, extract, or redact, and watch it call the right tool automatically.

Get started with the docs → https://docs.zerogpu.ai/integrations/mcp

Read the cookbook → https://docs.zerogpu.ai/cookbook/claude-iab-targeted-copy

Learn more about ZeroGPU → https://zerogpu.ai