mcp-multi-model

Give Claude Code superpowers — image gen, video gen, web search, and smart multi-model routing.

One MCP server. All the models you need. Zero tab-switching.

npx mcp-multi-model

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What can it do?

🎨 Generate images and videos — right in the terminal

"Generate a macOS app icon with a glowing indigo orb"

Claude calls Imagen 4 / GPT Image / Nano Banana, saves the PNG, and opens it. No browser, no Figma, no context switch.

Video too — Veo 3.1 generates short clips from a text prompt.

🧠 Smart routing — the right model for the job

Need reasoning / agentic coding → it routes to OpenAI GPT-5 / o-series (auto-handles max_completion_tokens, skips temperature where unsupported). Tell Claude to research something → it routes to Gemini (Google Search grounding). Ask it to write code cheaply → it routes to DeepSeek (fast, cheap, great at code). Need real-time info in Chinese → it routes to Kimi (web search).

You don't pick the model. The routing does it for you.

⚖️ Compare models side by side

"Ask both DeepSeek and Gemini how to implement a B-tree"

Two answers, one terminal. See which model gives you a better solution.

🌐 Web search built in

Gemini uses Google Search grounding. Kimi searches the Chinese web. No separate browser-use MCP needed.

🔧 One-line install

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

That's it. No git clone, no build step.

Supported Models

12+ providers preconfigured in config.example.yaml. Models without an API key are skipped automatically.

| Provider | Adapter | Why use it | |---|---|---| | OpenAI | openai | GPT-5 / GPT-5.5 reasoning, o1 / o3 / o4 series, GPT Image. Reasoning param handling is automatic (max_completion_tokens, temperature skipped where unsupported). | | Gemini | gemini | Long context, Google Search grounding. Image (Imagen 4 Fast / Ultra, Nano Banana 2) and video (Veo 3.1) generation built in. | | DeepSeek | openai | Code, math, logic — extremely low cost | | Kimi (Moonshot) | openai | Chinese web search, real-time info, tool-calling loop | | Grok (xAI) | openai | Real-time X/Twitter context, reasoning | | Perplexity | openai | Sonar models with built-in web search and citations | | Anthropic (via OpenRouter) | openai | Claude models routed through OpenRouter | | Mistral / Groq / Qwen / GLM / Together | openai | EU AI, ultra-fast inference, Chinese-native, open-source aggregators | | Ollama / LM Studio / llama.cpp / vLLM | openai | Local — no API key, no cost, full privacy |

Adding a new model is one block in config.yaml — see Configuration.

MCP Tools

Tools are dynamically generated from your config. With the default setup:

| Tool | What it does | |------|-------------| | 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 — auto-picks the best model for the task | | generate_image | Text → image via Gemini Imagen | | 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 |

Installation

Option 1: npx (recommended)

Add to your Claude Code MCP config (~/.mcp.json):

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

Option 2: Clone and run locally

git clone https://github.com/K1vin1906/mcp-multi-model.git
cd mcp-multi-model
npm install
npm run setup   # Interactive setup wizard — validates your API keys

Then add to your MCP config:

{
  "mcpServers": {
    "multi-model": {
      "command": "node",
      "args": ["/path/to/mcp-multi-model/index.js"]
    }
  }
}

API keys can be set via env in the config above, or in a .env file in the project directory.

Configuration

cp config.example.yaml config.yaml

defaults:
  max_tokens: 4000
  temperature: 0.7
  timeout_ms: 60000
  max_retries: 2
  # cache_ttl_ms: 300000   # Cache identical prompts for 5 min
  # daily_budget_usd: 5.0  # Daily spending limit in USD

models:
  deepseek:
    name: DeepSeek
    adapter: openai
    endpoint: https://api.deepseek.com/chat/completions
    api_key_env: DEEPSEEK_API_KEY
    model: deepseek-chat
    description: "Code, math, logic. Low cost."
    fallback_to: gemini
    pricing:
      input: 0.14    # $/M tokens
      output: 0.28

  gemini:
    name: Gemini
    adapter: gemini
    endpoint: https://generativelanguage.googleapis.com/v1beta
    api_key_env: GEMINI_API_KEY
    model: gemini-2.5-flash-preview-04-17
    description: "Long context, broad knowledge, Google Search."
    features:
      - google_search
    pricing:
      input: 0.10
      output: 0.40

  # Local models — no API key needed:
  # ollama:
  #   name: Ollama
  #   adapter: openai
  #   endpoint: http://localhost:11434/v1/chat/completions
  #   model: llama3.2

Image Generation

Two endpoint families are routed automatically based on the model ID:

Gemini family (uses GEMINI_API_KEY)

| Model ID | Endpoint | Notes | |---|---|---| | imagen-4-fast | :predict | Default, ~$0.02/image | | imagen-4-ultra | :predict | 2K quality, ~$0.06/image | | gemini-2.5-flash-image (Nano Banana) | :generateContent | Fast (~3s), 2,000 RPM free tier | | gemini-3-pro-image-preview (Nano Banana 2) | :generateContent | High quality, 500 RPM |

OpenAI family (uses OPENAI_API_KEY)

| Model ID | Endpoint | Notes | |---|---|---| | gpt-image-2 | /v1/images/generations | Best text rendering. Requires OpenAI org verification. |

Supports aspect_ratio: 1:1, 3:2, 4:3, 16:9, 9:16. quality and size forwarded to OpenAI image endpoints.

Video Generation

Generate short video clips using Gemini Veo 3.1 (uses GEMINI_API_KEY).

| Parameter | Type | Notes | |-----------|------|-------| | prompt | string | Text description of the desired video | | aspect_ratio | 16:9 / 9:16 / 1:1 | | | duration | 4 / 6 / 8 (seconds) | Must be even — Veo only accepts even durations | | save_path | string? | Defaults to /tmp/mcp-media/videos/ |

Local Models

Any OpenAI-compatible local runner works — Ollama, LM Studio, llama.cpp, vLLM:

models:
  ollama:
    name: Ollama
    adapter: openai
    endpoint: http://localhost:11434/v1/chat/completions
    model: llama3.2

Mix local and cloud models freely — use ask_all to compare Ollama vs DeepSeek vs Gemini in one call.

Built-in Features

• Auto-retry & fallback — Exponential backoff on 429/5xx, automatic fallback to backup model
• Conversation history — Multi-turn context with conversation_id (30min expiry, up to 10 turns)
• Cost tracking — Per-call token usage and cost estimation
• Response caching — Cache identical prompts with configurable TTL
• Daily budget limit — Set a spending cap; calls are blocked when exceeded
• Streaming — Real-time SSE streaming for all adapters

Privacy

This is a local relay. No telemetry, no analytics, no data sent to the extension author. Prompts go directly from your machine to the LLM provider you configured.

Full policy: k1vin1906.github.io/mcp-multi-model/privacy.html

License

MIT