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This guide shows how to combine ZeroGPU’s zlm-v1-iab-classify-edge model with Claude entirely inside Claude Desktop, with no code to write or run. You connect the hosted ZeroGPU MCP server once, then Claude reads your article, calls the IAB classifier as a tool to get hard signals (IAB content categories, audience segments, and confidence scores), and uses those signals to write an Ad Brief, a Newsletter Blurb, and a Content Pitch, each in a distinct voice. By pairing ZeroGPU’s edge classifier with Claude’s writing, this guide walks you through a transparent, reproducible pipeline where the classification numbers visibly drive every line of copy, all from a chat window. For the full reference, see the zlm-v1-iab-classify-edge model card and the ZeroGPU MCP server guide. In this guide, you’ll explore:
  • ZeroGPU IAB Classifier (zlm-v1-iab-classify-edge): An edge-served model that maps any text straight to the IAB Content Taxonomy, returning content categories, audience segments, and a per-label confidence score in a single call. No instructions or prompt engineering needed, you send text and get structured signals back. See the model card.
  • ZeroGPU MCP Server: The hosted Model Context Protocol server at https://mcp.zerogpu.ai/mcp that exposes ZeroGPU’s edge models, including the IAB classifier as zerogpu_classify_iab, as MCP tools any client can call. Connect it once and Claude Desktop can reach the classifier directly. See the MCP server guide.
  • ZeroGPU: An ultra-fast, compute-efficient inference provider for apps and agents. We run purpose-built small and nano language models across an edge-powered network for the high-volume, purpose-specific tasks your app or agent runs constantly. Plug in our OpenAI-compatible API and you’re live - zero GPU infrastructure, serverless, auto-scaling by default.
  • Claude Desktop: Anthropic’s desktop app for Claude. Here it orchestrates the pipeline: it connects to the ZeroGPU MCP server, calls the classifier as a tool, reads the returned IAB categories, audience segments, and confidence scores, and writes three distinct outputs grounded in those numbers.
This setup not only demonstrates a practical application of signal-driven copywriting, but also provides a flexible framework that can be adapted to other real-world scenarios requiring classification signals to steer content generation.

🎥 Watch the Video Guide

Prefer a quick walkthrough? Watch the full demo here:

🔑 Get Your ZeroGPU API Key

You’ll need a ZeroGPU API key and Project ID so Claude Desktop can authenticate to the MCP server. This is the only credential setup required, there are no packages to install and no keys to paste into code. You can go to here to get an API key and Project ID from ZeroGPU. The key starts with zgpu-api- and the Project ID (UUID) is on the project settings page.
  1. Sign in to the ZeroGPU dashboard.
  2. Open API Keys and click Create key.
  3. Copy the key (starts with zgpu-api-) and grab your Project ID (UUID) from the project settings page.
Keep both values handy for the next step. The classifier is reached through the hosted ZeroGPU MCP server, so no Anthropic API key or SDK is required, you just need the desktop app.

🔌 Connect the ZeroGPU MCP Server in Claude Desktop

The ZeroGPU MCP server is hosted at https://mcp.zerogpu.ai/mcp and authenticates with two headers on every request: x-api-key (your API key) and x-project-id (your Project ID). To make its tools available to Claude, add the server to Claude Desktop’s MCP configuration. In Claude Desktop, open Settings → Developer → Edit Config, then add the zerogpu entry to mcpServers (substitute your real key and Project ID):
{
  "mcpServers": {
    "zerogpu": {
      "type": "http",
      "url": "https://mcp.zerogpu.ai/mcp",
      "headers": {
        "x-api-key": "<YOUR_API_KEY>",
        "x-project-id": "<YOUR_PROJECT_ID>"
      }
    }
  }
}
Save the file and restart Claude Desktop. Once it reconnects, the ZeroGPU tools (including zerogpu_classify_iab) appear in the tools menu of any chat, and Claude can call them on its own. For the request and response shape behind the tool, see the model card; for the full tool catalog, see the MCP server guide.

🏷️ Classify Content with the ZeroGPU MCP Server

ZeroGPU is an ultra-fast, compute-efficient inference provider for apps and agents. We run purpose-built small and nano language models across an edge-powered network for the high-volume, purpose-specific tasks your app or agent runs constantly. Plug in our OpenAI-compatible API and you’re live - zero GPU infrastructure, serverless, auto-scaling by default. In this section, we will classify a short snippet of content against the IAB Content Taxonomy as a standalone example, straight from the Claude Desktop chat. zlm-v1-iab-classify-edge maps your input directly to the taxonomy, so you only send text and get back audience segments and content categories (in both iab_1_0 and iab_2_2 versions), each with a confidence score. To try it, just ask Claude to classify a sentence:
Classify this against the IAB taxonomy with ZeroGPU:

"The Arc 2 smartwatch tracks heart-rate variability, blood oxygen, and
sleep stages on-device, with a nine-day battery and a $249 price."
Claude picks the zerogpu_classify_iab tool and calls it with your text:
{
  "name": "zerogpu_classify_iab",
  "arguments": {
    "text": "The Arc 2 smartwatch tracks heart-rate variability, blood oxygen, and sleep stages on-device, with a nine-day battery and a $249 price."
  }
}
The tool result is a content block whose body parses to the structured signals:
{
  "audience": [
    { "name": "Technology & Computing", "score": 0.7821 },
    { "name": "Consumer Electronics",   "score": 0.7402 },
    { "name": "Healthy Living",          "score": 0.6233 },
    { "name": "25-34",                   "score": 0.5990 }
  ],
  "content": {
    "iab_1_0": [
      { "name": "Wearable Technology",   "score": 0.7689 },
      { "name": "Technology & Computing", "score": 0.7421 }
    ],
    "iab_2_2": [
      { "name": "Wearable Technology",   "score": 0.7689 },
      { "name": "Smartphones",            "score": 0.7115 },
      { "name": "Technology & Computing", "score": 0.6902 }
    ]
  }
}
🎉 ZeroGPU effortlessly turns a sentence into IAB categories, audience segments, and confidence scores in one call, the structured signal layer your copy pipeline runs on!

🎯 Turn One Article into Three Targeted Outputs

This section hands Claude a full article and one plain prompt, lets it call the ZeroGPU classifier as a tool, and turns the returned signals into three clearly different pieces of copy. For the classifier to be more than decoration, Claude has to actually call it and then let the numbers steer the writing. The cleanest way to lock that behavior in is a Claude Desktop Project: create one and paste the following into its custom instructions, so every chat in the project follows the same steps.
You are a content strategist.

For every article you are given, follow these steps in order:

1. Call the `zerogpu_classify_iab` tool with the article text. It returns IAB
   content categories, audience segments, and a confidence score (0-1) for each.

2. Use those signals explicitly to write THREE outputs. The highest-confidence
   content category and audience segments must visibly shape each one, and you
   should name the categories, segments, and scores you leaned on.

   - Ad Brief        -> persuasive, conversion-focused. Lead with the target
                        segments, a sharp hook, and a clear call to action.
   - Newsletter Blurb -> editorial, engaging. A short, readable paragraph with
                        a headline, written for a curious reader, not a buyer.
   - Content Pitch   -> strategic, high-level. An angle, a "why now", the
                        audience it serves, and where it should be distributed.

Format the response with these exact section markers and nothing before them:
=== AD BRIEF ===
=== NEWSLETTER BLURB ===
=== CONTENT PITCH ===
With the project set up, each run is a single, copy-pasteable message. There are no hidden steps: you paste your article in place of [article text] and Claude does the rest.
Here is my content: [article text]
Classify it and return:
1. Ad Brief
2. Newsletter Blurb
3. Content Pitch
If you’d rather not create a project, you can paste the strategist instructions at the top of the same message instead, just keep them above the article. Either way Claude reaches the classifier through the zerogpu_classify_iab tool, so the model never touches your ZeroGPU keys, it just asks for a classification and gets the structured result back. For the example, here is one real article, a product announcement, pasted in place of [article text]:
Pulse Labs today unveiled the Arc 2, a smartwatch that pushes continuous
health monitoring further than anything in its price class. The Arc 2 tracks
heart-rate variability, blood-oxygen saturation, skin temperature, and sleep
stages around the clock, then surfaces the trends in a redesigned companion
app. A new on-device chip runs the analysis locally, so most insights appear
without ever leaving the wrist. Battery life climbs to nine days on a single
charge, and the titanium case shaves four grams off the previous model. At
$249, the Arc 2 undercuts every flagship wearable while matching their sensor
suite, a bet that the next wave of buyers cares more about daily health
signals than about another screen to check.
Claude calls zerogpu_classify_iab and the classifier returns the signals it builds on:
{
  "audience": [
    { "name": "Technology & Computing", "score": 0.7821 },
    { "name": "Consumer Electronics",   "score": 0.7402 },
    { "name": "Healthy Living",          "score": 0.6233 },
    { "name": "25-34",                   "score": 0.5990 }
  ],
  "content": {
    "iab_1_0": [
      { "name": "Wearable Technology",   "score": 0.7689 },
      { "name": "Technology & Computing", "score": 0.7421 }
    ],
    "iab_2_2": [
      { "name": "Wearable Technology",   "score": 0.7689 },
      { "name": "Smartphones",            "score": 0.7115 },
      { "name": "Technology & Computing", "score": 0.6902 }
    ]
  }
}
And here are the three outputs Claude writes from them. Notice how each one names the categories and segments it leaned on, and how the voice shifts between them:
=== AD BRIEF ===
Campaign: Pulse Labs Arc 2 launch
Lead segments: Technology & Computing (0.78), Consumer Electronics (0.74),
Healthy Living (0.62), ages 25-34 (0.60)
Buy against: Wearable Technology (0.77) and Consumer Electronics inventory,
programmatic display + paid social.

Hook: Flagship health sensors. Nine-day battery. $249.
Body: The Arc 2 tracks HRV, blood oxygen, skin temperature, and sleep on-device,
so the insight lands on your wrist, not in the cloud. The same sensor suite the
flagships charge double for. Built for the 25-34 buyer who tracks their health
daily and does not want to pay a flagship tax to do it.
CTA: Pre-order the Arc 2 - ships next week.

=== NEWSLETTER BLURB ===
The wearable that bets on your health, not your attention

Most smartwatches want one more glance from you. Pulse Labs went the other way.
The new Arc 2 reads heart-rate variability, blood oxygen, skin temperature, and
sleep through the night, runs the analysis on a new on-device chip, and only
surfaces what matters. Nine days of battery, a lighter titanium case, and a $249
price that quietly undercuts every flagship. One for the Healthy Living crowd
who want the sensor suite without the screen-time.

=== CONTENT PITCH ===
Working title: "The $249 line in the sand for health wearables"

Angle: Pulse Labs is reframing the category around daily health signal rather
than notifications - on-device analysis, longer battery, flagship sensors at
half the price.
Why now: The Arc 2 launch gives a concrete hook for a Wearable Technology piece
(top category, 0.77) that the Technology & Computing audience (0.78) already
follows closely.
Audience: 25-34, health-conscious tech buyers (Healthy Living, 0.62) who weigh
sensor quality against price.
Distribution: Lead on consumer-tech and wearables verticals; cross-post to
health-and-fitness newsletters where the Healthy Living segment over-indexes.
🎉 From one article and one plain prompt, the classifier produced hard signals and Claude turned them into a persuasive ad brief, an editorial blurb, and a strategic pitch, each visibly anchored to the same IAB categories and audience scores.

🧩 Plug In Your Content Source and Output Destinations

The example above pastes one article into the chat and reads the outputs back. In a real workflow you’ll want to pull content from somewhere and push the results somewhere, and because everything runs through MCP, you can do both without writing code, just by adding more MCP servers to Claude Desktop alongside ZeroGPU.
  • Sending content in: Connect an MCP server for your content source (a CMS, a Google Drive, an RSS or web-fetch server) and ask Claude to grab the latest article, then run it through the same strategist steps. The IAB classifier sees whatever text Claude hands it.
  • Sending outputs out: Connect MCP servers for your destinations (an email tool, an ads platform, a docs or CMS server) and tell Claude where each section goes, for example “send the Ad Brief to the campaign draft, the Newsletter Blurb to the email block, and the Content Pitch to a new doc.”
Because the classifier returns plain structured signals, you can also branch on them in plain language before any copy is written, for example only producing the newsletter blurb when a target segment clears a confidence threshold:
Classify this article, then only write the Newsletter Blurb if the top audience
segment scores at least 0.6. Otherwise just tell me the top segment and its score.
Top audience segment: Technology & Computing (0.7821) - clears 0.6, writing the
Newsletter Blurb now.
🎉 The same three-output pipeline now reads from your content source and writes to your channels, with the IAB scores available as routing logic at every step, all orchestrated by Claude over MCP.

🚀 Go Deeper

This pipeline is a foundation. A few directions to extend it:
  • Content monetisation: Attach the returned IAB categories to each article as ad-targeting metadata, then use the confidence scores to pick the highest-value inventory automatically. Pages that classify cleanly into premium categories (for example Consumer Electronics at high confidence) can be routed to higher-CPM ad slots, while low-confidence pages fall back to broad inventory.
  • Personalization pipelines: Run every incoming article through the classifier, then store the audience segments per piece. When a reader arrives, match their on-site behavior to those segments and let Claude rewrite the same blurb in the voice that segment responds to, turning one article into many audience-specific variants.
  • Audience segmentation workflows: Batch-classify your whole content library and cluster pieces by their iab_2_2 categories and audience segments. The clusters become ready-made segments for email lists, ad campaigns, and editorial calendars, and Claude can draft a tailored pitch for each cluster from its dominant signals.
For high-volume runs, pair this with the ZeroGPU Batch API to classify thousands of articles in one asynchronous job before the copy step.

🌟 Highlights

This guide has walked you through combining ZeroGPU’s IAB classifier with Claude inside Claude Desktop to turn one article into three targeted outputs driven by real classification signals, with no code to write or run. You can adapt and expand this example for various other scenarios requiring classification signals to steer content generation. Key tools utilized in this guide include:
  • ZeroGPU IAB Classifier (zlm-v1-iab-classify-edge): An edge-served model that maps any text straight to the IAB Content Taxonomy, returning content categories, audience segments, and a per-label confidence score in a single call. No instructions or prompt engineering needed, you send text and get structured signals back. See the model card.
  • ZeroGPU MCP Server: The hosted Model Context Protocol server at https://mcp.zerogpu.ai/mcp that exposes ZeroGPU’s edge models, including the IAB classifier as zerogpu_classify_iab, as MCP tools any client can call. Connect it once and Claude Desktop can reach the classifier directly. See the MCP server guide.
  • ZeroGPU: An ultra-fast, compute-efficient inference provider for apps and agents. We run purpose-built small and nano language models across an edge-powered network for the high-volume, purpose-specific tasks your app or agent runs constantly. Plug in our OpenAI-compatible API and you’re live - zero GPU infrastructure, serverless, auto-scaling by default.
  • Claude Desktop: Anthropic’s desktop app for Claude. Here it orchestrates the pipeline: it connects to the ZeroGPU MCP server, calls the classifier as a tool, reads the returned IAB categories, audience segments, and confidence scores, and writes three distinct outputs grounded in those numbers.
This comprehensive setup allows you to adapt and expand the example for various scenarios requiring classification signals to steer content generation.