# mcp-multi-model [Health: Active]

**Category:** 🗣️ Conversational AI  
**Repository:** https://github.com/K1vin1906/mcp-multi-model  
**GitHub Stars:** 6  
**npm Downloads (last month):** 1155  
**Views:** 0  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/mcp-multi-model

## Description
Give your AI coding agent superpowers — parallel multi-model queries, smart routing, and image/video generation across OpenAI (GPT-6, GPT-5.6, GPT Image 2), Gemini (Nano Banana, Veo), DeepSeek, Kimi and any OpenAI-compatible endpoint. Works with Claude Co

## Tools
Capabilities this server exposes over MCP:

- **ask_ai** — Query any model — unified entry with `temperature` / `top_p` control
- **ask_deepseek** — Query DeepSeek directly
- **ask_gemini** — Query Gemini directly
- **ask_kimi** — Query Kimi directly
- **ask_all** — Query all models in parallel, compare results
- **ask_both** — Query any two models in parallel
- **delegate** — Smart routing — offloads a self-contained task to the best-fit model by category
- **generate_image** — Text → image via Gemini Nano Banana (default: Nano Banana 2 Lite)
- **generate_video** — Text → video via Gemini Veo
- **translate** — CN ↔ EN translation
- **research** — Deep research with web search
- **check_health** — Ping all models, report status and latency

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `npx` (confidence: high):

```json
"mcpServers": {
  "mcp-multi-model": {
    "command": "npx",
    "args": ["-y","mcp-multi-model"]
  }
}
```

## Documentation

## What mcp-multi-model does

mcp-multi-model exposes configurable model providers as MCP tools for an AI coding agent or another MCP host. It supports direct queries, parallel comparisons, category-based delegation, translation, research, health checks, and media generation.

The provider set includes OpenAI, Gemini, DeepSeek, Kimi, Grok, Perplexity, OpenRouter-hosted Anthropic models, and several other OpenAI-compatible services. Local endpoints such as Ollama, LM Studio, llama.cpp, and vLLM can also be configured without an API key. Providers without credentials are skipped automatically.

## How it works

Tools are generated from the server's YAML configuration. The default setup includes a unified `ask_ai` entry, provider-specific query tools, `ask_all`, `ask_both`, `delegate`, `research`, `translate`, `generate_image`, `generate_video`, and `check_health`.

`delegate` selects a configured model according to a task category such as code, research, creative work, or realtime information. `ask_all` and `ask_both` send requests in parallel so responses can be compared. Chat tools accept an effort setting, with `fast` and `deep` mapped to provider-specific reasoning controls.

Web-enabled configurations use provider features rather than a separate browser server. Gemini can use Google Search grounding, OpenAI Responses models can use web search with source URLs, and Kimi can search the Chinese web through a tool-calling loop.

## Setup and configuration

The quickest installation runs the package through npx:

```json
{
  "mcpServers": {
    "multi-model": {
      "command": "npx",
      "args": ["-y", "mcp-multi-model"],
      "env": {
        "DEEPSEEK_API_KEY": "sk-...",
        "GEMINI_API_KEY": "AI..."
      }
    }
  }
}
```

API credentials can be placed in the MCP host configuration or in a `.env` file in the project directory. A local installation is also available by cloning the repository, running `npm install`, and then using `npm run setup` for the interactive setup wizard. The server can subsequently be started with Node and its `index.js` file.

Provider definitions, routing categories, and model choices are configured in `config.yaml`; an example configuration contains more than a dozen provider options. Adding a model involves adding a configuration block.

## Tools and capabilities

- Query a selected model with `ask_ai` or a provider-specific tool.
- Compare every configured model with `ask_all`, or compare two with `ask_both`.
- Delegate a self-contained task to a category-selected model.
- Generate images with Gemini image models and videos with Veo.
- Run web-backed research using configured search-capable providers.
- Translate between Chinese and English.
- Check provider availability and latency with `check_health`.
- Control supported reasoning behavior with `effort: "fast"` or `effort: "deep"`.

## Limitations and notes

The server communicates over stdio MCP, so the host controls execution timeouts. Parallel queries can take several seconds, deeper requests can take longer, and video generation may take several minutes. Some clients also differ in whether they read the server's built-in instructions.

Provider quality, speed, pricing, search behavior, and supported parameters vary by model. The adapters handle some provider differences, including OpenAI reasoning parameters and unsupported temperature settings. Local providers require their own compatible endpoint to be available.

The included benchmark used a small set of tasks and samples, so its scores should be treated as configuration guidance rather than a general evaluation of every model or workload.

_Full upstream README: https://allmcps.com/mcp/mcp-multi-model/readme_