Hf Model Explorer

An MCP server that lets AI agents search, compare, and right-size Hugging Face models — including estimating whether a model will actually fit in your available GPU VRAM before you download 15GB of weights and find out the hard way.

Works with any MCP-compatible client: Claude, Google Antigravity, Cursor, and others.

Why

Picking a model off Hugging Face usually means checking the model card, guessing at parameter count, mentally doing GB-per-precision math, and hoping it fits your GPU. This wraps that into tools an agent can call directly, so you can ask things like:

"Find me a small instruction-tuned model I can run locally on a 12GB GPU"

and get an actual, sized answer instead of a guess.

Tools

Install

pip install hf-model-explorer-mcp

Or run directly without installing, via uvx:

uvx hf-model-explorer-mcp

Configure

Claude Desktop / Claude Code

Add to your MCP config (claude_desktop_config.json or .mcp.json):

{
  "mcpServers": {
    "hf-model-explorer": {
      "command": "uvx",
      "args": ["hf-model-explorer-mcp"]
    }
  }
}

Google Antigravity

Add to ~/.gemini/antigravity/mcp_config.json (path may vary by Antigravity version — check Settings → MCP Servers in the IDE):

{
  "mcpServers": {
    "hf-model-explorer": {
      "command": "uvx",
      "args": ["hf-model-explorer-mcp"]
    }
  }
}

Example

User: I want to run an instruction-tuned 7B-ish model locally. I have a 12GB GPU.

Agent calls: search_models(query="instruct", task="text-generation")
Agent calls: check_hardware_fit(model_id="mistralai/Mistral-7B-Instruct-v0.2", available_vram_gb=12)

→ {
    "parameters": "7.0B",
    "recommended_precision": "fp16",
    "estimated_vram_by_precision_gb": { "fp16": 15.65, "int8": 7.82, "int4": 3.91 },
    "fits": true
  }

How hardware-fit estimation works

Parameter count comes from the model's safetensors metadata on the Hub. Memory is estimated as params × bytes_per_param × 1.2 (a 20% overhead margin for activations and KV-cache), using standard bytes-per-parameter figures:

This is an estimate, not a guarantee — actual usage varies by framework, batch size, and context length. Models without safetensors metadata return an explicit "unknown" rather than a guessed number.

Development

git clone https://github.com/YOUR_USERNAME/hf-model-explorer-mcp
cd hf-model-explorer-mcp
pip install -e ".[dev]"
pytest

All 23 tests mock the Hugging Face API, so the suite runs offline with no rate limits.

Publishing (for your own fork)

1. PyPI: python -m build && twine upload dist/*
2. MCP Registry: update server.json with your GitHub username and package version, then follow the MCP Registry publishing guide to submit it — typically via mcp-publisher authenticated against your GitHub repo.

License

MIT