Oumi MCP Server

An MCP (Model Context Protocol) server that gives AI coding assistants access to Oumi's library of ~500 ready-to-use YAML configs for fine-tuning LLMs.

When connected to Cursor, Claude Desktop, or any MCP-compatible client, the server lets the AI search for training recipes, retrieve full YAML configs, validate them, and follow guided ML engineering workflows -- all without you having to browse docs manually.

What it does

The server exposes 5 tools and 6 resources over MCP:

Supported models

Llama 3.1/3.2/4, Qwen 3, Phi 4, Gemma 3, DeepSeek R1, SmolLM, and more.

Supported training techniques

SFT, DPO, GRPO, KTO, LoRA, QLoRA, full fine-tuning, pretraining, evaluation, inference.

Installation

As part of Oumi (recommended)

pip install oumi[mcp]

Standalone

pip install oumi-mcp

From source (development)

git clone https://github.com/oumi-ai/oumi.git
cd projects/oumi-mcp
pip install -e .

Running the server

oumi-mcp

Or run as a Python module:

python -m oumi_mcp_server

Connecting to an MCP client

Cursor

Add to your Cursor MCP settings (.cursor/mcp.json):

{
  "mcpServers": {
    "oumi": {
      "command": "oumi-mcp"
    }
  }
}

Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "oumi": {
      "command": "oumi-mcp"
    }
  }
}

Any MCP client (stdio transport)

The server uses stdio transport by default. Point your MCP client to the oumi-mcp command.

How configs work

The server ships with a bundled snapshot of Oumi's ~500 YAML config files. On startup, it checks for a fresher cached copy and syncs from GitHub if the cache is stale (older than 24 hours). The resolution order is:

1. OUMI_MCP_CONFIGS_DIR environment variable (explicit override)
2. ~/.cache/oumi-mcp/configs (synced from GitHub, refreshed every 24h)
3. Bundled configs shipped with the package (always-available fallback)

This means:

• The server works immediately after install, even offline
• Configs stay up-to-date automatically via lazy background sync
• You can pin a specific config directory with the env var if needed

Force a sync

To manually refresh configs, delete the cache and restart:

rm -rf ~/.cache/oumi-mcp
oumi-mcp

Example workflow

Once connected, ask your AI assistant something like:

"Find me a LoRA config for fine-tuning Llama 3.1 8B on my custom dataset"

The assistant will use the MCP tools to:

1. search_configs(model="llama3_1", query="8b_lora", task="sft") -- find matching recipes
2. get_config("llama3_1/sft/8b_lora", include_content=True) -- retrieve the full YAML
3. Help you customize model_name, datasets, output_dir, etc.
4. validate_config("/path/to/your/config.yaml", "training") -- validate before running

Configuration

Project structure

oumi-mcp/
  src/oumi_mcp_server/
    __init__.py          # Package metadata
    __main__.py          # python -m entry point
    server.py            # MCP server, tools, resources, config sync
    config_service.py    # Config parsing, search, metadata extraction
    constants.py         # Type definitions and constants
    models.py            # TypedDict data models
    prompts/
      mle_prompt.py      # ML engineering workflow guidance resources
    configs/             # Bundled YAML configs (~500 files)
      recipes/           # Model-specific training recipes
      apis/              # API provider configs
      examples/          # Example configs
  pyproject.toml

Development

# Install in development mode
pip install -e ".[dev]"

# Run the server
oumi-mcp

# Run tests
pytest

Versioning

This package follows semantic versioning. The version is independent from the main oumi package but tracks compatibility:

• oumi-mcp 0.x.y is compatible with oumi >= 0.6.0
• Configs are synced from the oumi main branch and stay current regardless of package version
• Bump the oumi-mcp version when the server code, tools, or resources change

License

Apache-2.0 -- see the main Oumi repository for details.