Model Context Protocol (MCP)

LLM Gateway provides a Model Context Protocol (MCP) server that enables AI assistants like Claude Code to access multiple LLM providers through a unified interface. This allows you to use any model from OpenAI, Anthropic, Google, and more directly from your AI coding assistant.

What is MCP?

The Model Context Protocol (MCP) is an open standard that allows AI assistants to connect with external tools and data sources. LLM Gateway's MCP server exposes tools for:

• Chat completions - Send messages to any supported LLM
• Image generation - Generate images using models like Qwen Image
• Nano Banana image generation - Generate images with Gemini 3 Pro Image Preview and optionally save to disk
• Model discovery - List available models with capabilities and pricing

Available Tools

chat

Send a message to any LLM and get a response.

Parameters:

• model (string) - The model to use (e.g., "gpt-4o", "claude-sonnet-4-20250514")
• messages (array) - Array of messages with role and content
• temperature (number, optional) - Sampling temperature (0-2)
• max_tokens (number, optional) - Maximum tokens to generate

Example:

{
	"model": "gpt-4o",
	"messages": [{ "role": "user", "content": "Explain quantum computing" }],
	"temperature": 0.7
}

generate-image

Generate images from text prompts using AI image models.

Parameters:

• prompt (string) - Text description of the image to generate
• model (string, optional) - Image model (default: "qwen-image-plus")
• size (string, optional) - Image size (default: "1024x1024")
• n (number, optional) - Number of images (1-4, default: 1)

Example:

{
	"prompt": "A serene mountain landscape at sunset",
	"model": "qwen-image-max",
	"size": "1024x1024"
}

generate-nano-banana

Generate an image using Gemini 3 Pro Image Preview ("Nano Banana"). Returns an inline image preview, and optionally saves the image to disk when the server is configured with an upload directory.

Parameters:

• prompt (string) - Text description of the image to generate
• filename (string, optional) - Filename for the saved image, no path separators allowed (default: nano-banana-{timestamp}.png)
• aspect_ratio (string, optional) - Aspect ratio: "1:1", "16:9", "4:3", or "5:4"

Example:

{
	"prompt": "A pixel-art cat sitting on a rainbow",
	"filename": "hero-image.png",
	"aspect_ratio": "16:9"
}

list-models

List available LLM models with capabilities and pricing.

Parameters:

• include_deactivated (boolean, optional) - Include deactivated models
• exclude_deprecated (boolean, optional) - Exclude deprecated models
• limit (number, optional) - Maximum models to return (default: 20)
• family (string, optional) - Filter by family (e.g., "openai", "anthropic")

list-image-models

List all available image generation models.

Example output:

# Image Generation Models

## Qwen Image Plus
- **Model ID:** `qwen-image-plus`
- **Description:** Text-to-image with excellent text rendering
- **Price:** $0.03 per request

## Qwen Image Max
- **Model ID:** `qwen-image-max`
- **Description:** Highest quality text-to-image
- **Price:** $0.075 per request

Setup

claude mcp add --transport http --scope user llmgateway https://api.llmgateway.io/mcp \
  --header "Authorization: Bearer your-api-key-here"

{
  "mcpServers": {
    "llmgateway": {
      "url": "https://api.llmgateway.io/mcp",
      "headers": {
        "Authorization": "Bearer your-api-key-here"
      }
    }
  }
}

codex mcp add llmgateway --url https://api.llmgateway.io/mcp \
  --bearer-token-env-var LLM_GATEWAY_API_KEY

[mcp_servers.llmgateway]
url = "https://api.llmgateway.io/mcp"
bearer_token_env_var = "LLM_GATEWAY_API_KEY"

{
  "mcpServers": {
    "llmgateway": {
      "url": "https://api.llmgateway.io/mcp",
      "headers": {
        "Authorization": "Bearer your-api-key-here"
      }
    }
  }
}

curl -X POST https://api.llmgateway.io/mcp \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-api-key" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/list"
  }'

curl -N https://api.llmgateway.io/mcp \
  -H "Accept: text/event-stream" \
  -H "Authorization: Bearer your-api-key"

Use Cases

Multi-Model Access in Claude Code

Use Claude Code to interact with models it doesn't natively support:

Use the chat tool with model "gpt-4o" to analyze this code for security issues.

Image Generation

Generate images directly from your AI assistant:

Use generate-image to create a logo for my new startup.
It should be minimalist, blue and white, representing AI and cloud computing.

Nano Banana (Gemini Image Generation)

Generate images with Gemini 3 Pro for use in your project:

Use generate-nano-banana to create a hero image for my landing page with a 16:9 aspect ratio.

Cost-Effective Model Selection

Query available models to find the best option for your task:

List models from OpenAI and Anthropic, then use the cheapest one for this simple task.

Authentication

The MCP server supports two authentication methods:

1. Bearer Token - Authorization: Bearer your-api-key
2. API Key Header - x-api-key: your-api-key

Your API key is the same one you use for the REST API and works across all LLM Gateway services.

OAuth Support

For applications that prefer OAuth authentication, LLM Gateway's MCP server implements OAuth 2.0:

• Authorization Endpoint: /oauth/authorize
• Token Endpoint: /oauth/token
• Registration Endpoint: /oauth/register
• Supported Flows: Authorization Code, Client Credentials

Enabling Local Image Saving

By default, generate-nano-banana returns images inline without writing to disk. To enable saving generated images to the server filesystem, the UPLOAD_DIR environment variable must be set on the gateway host at startup. This is a server-side setting — it cannot be configured from the client.

This is only possible for self-hosted MCP deployments. Configure UPLOAD_DIR using your deployment method:

• Docker: Pass -e UPLOAD_DIR=/data/images or add it to your docker-compose.yml environment section.
• systemd: Add Environment=UPLOAD_DIR=/data/images to your service unit file.
• .env file: Add UPLOAD_DIR=/data/images to the .env file loaded by your gateway process.

Troubleshooting

Connection Errors

If you're having trouble connecting:

1. Verify your API key is valid
2. Check the endpoint URL is correct: https://api.llmgateway.io/mcp
3. Ensure your firewall allows outbound HTTPS connections

Tool Not Found

If tools aren't appearing:

1. Restart your MCP client
2. Check the configuration syntax
3. Verify the MCP server is responding: GET https://api.llmgateway.io/mcp

Rate Limiting

The MCP server respects your account's rate limits. If you're hitting limits:

1. Check your usage in the dashboard
2. Consider upgrading your plan
3. Implement request queuing in your application

Benefits

• Unified Access - Use 200+ models from 20+ providers through one interface
• Cost Tracking - Monitor usage and costs in the LLM Gateway dashboard
• Caching - Automatic response caching reduces costs and latency
• Fallback - Automatic provider failover ensures reliability
• Image Generation - Generate images directly from your AI assistant