Model Runner
Provides a unified MCP interface for running completions, embeddings, image generation, and classification across OpenAI, Anthropic, Groq, and Mistral. Eliminates provider-specific boilerplate by standardizing API calls for text generation, vector embeddings, and classification tasks.
README
Model Runner
Stop copy-pasting boilerplate every time you need to call a different AI model.
Model Runner is an MCP server that gives any AI assistant a unified interface to run completions, embeddings, image generation, and classification across all major providers. One tool call instead of per-provider API clients.
Quick Start
Add to your mcpServers config:
{
"mcpServers": {
"model-runner": {
"url": "https://your-cloud-run-url/mcp"
}
}
}
Or run locally:
npm install
npm start
Before / After
Before: Your assistant wants to classify customer feedback into sentiment buckets. It cannot call the OpenAI API natively, does not know the exact endpoint shape, and cannot handle auth headers.
// 30 lines of fetch boilerplate. Per provider. Per project.
// Auth headers, message array format, error shapes, all different.
After: One tool call:
{
"tool": "run_classification",
"arguments": {
"text": "Waited 40 minutes for support and got no answer",
"labels": ["positive", "negative", "neutral"],
"api_key": "sk-..."
}
}
Output:
{
"label": "negative",
"confidence": 0.97,
"reasoning": "The customer experienced a long wait with no resolution, indicating a clearly negative experience."
}
Tools
| Tool | What it does |
|---|---|
list_supported_models |
Browse the full catalog of models by provider and capability |
run_completion |
Text generation via OpenAI, Anthropic, Groq, or Mistral |
run_embedding |
Vector embeddings via OpenAI or Cohere |
run_image_generation |
Image generation via DALL-E 2 or DALL-E 3 |
estimate_tokens |
Estimate token count before making expensive API calls |
run_classification |
Zero-shot text classification with confidence scores |
Who is this for?
- AI assistant builders who want their agent to invoke ML models without hardcoding provider-specific API logic into every project
- Developers prototyping who need a quick way to compare outputs across OpenAI, Anthropic, Groq, and Mistral without writing multiple API clients
- Data teams running pipelines who want a single MCP endpoint to classify, embed, or summarize records at scale without managing provider SDKs
Health Check
Both endpoints return the same response and require no authentication:
GET /
GET /health
Response:
{
"status": "ok",
"server": "model-runner",
"version": "1.0.0",
"tools": 6
}
Built by
Mastermind HQ - AI tools built for builders.
License
MIT
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