NVIDIA NIM MCP Server

A production-ready Model Context Protocol (MCP) server for consuming NVIDIA NIM (NVIDIA Inference Microservices) models. Supports 50+ LLMs, multimodal models, image generation, embeddings, reranking, function calling, vision, and code-specialized models with rich metadata for intelligent agent selection.

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🚀 Features

• 10 MCP Tools: chat completion, text generation, embeddings, reranking, function calling, model listing, model info, image generation, image analysis, multimodal tasks, model comparison
• 50+ Supported Models: Llama 3.1/3.2, Nemotron 3 Ultra (550B), MiniMax M3, Kimi K2.6 (1T), DeepSeek V4 Pro, GLM 5.1, Qwen 3.5 397B, Mistral Large 3 (675B), GPT-OSS 120B, DiffusionGemma, FLUX.1, SDXL, SD3, and more
• Rich Model Metadata: licensing, hardware requirements, benchmarks, image generation specs, reasoning modes, tags for agent selection
• Advanced Filtering: by commercial use, reasoning, vision, function calling, multimodal, context length, tags, hardware
• Production-Grade: automatic retries with exponential backoff, per-minute rate limiting, structured JSON logging
• Type-Safe: full TypeScript, Zod input validation on every tool
• Docker-Ready: multi-stage Dockerfile with non-root user, health checks
• Configurable: all settings via environment variables
• Single Required Env: Only NVIDIA_API_KEY required; all others have sensible defaults

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📋 Prerequisites

• Node.js 18+ (for NPM installation) or Docker (for container deployment)
• A NVIDIA NGC API key (nvapi-...)

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⚙️ Installation

Option 1: NPM Global Installation (Recommended)

# Install globally
npm install -g nvidia-nim-mcp

# Run directly
nvidia-nim-mcp

Option 2: NPM Local Installation

# Initialize your project
npm init -y

# Install locally
npm install nvidia-nim-mcp

# Run with npx
npx nvidia-nim-mcp

Option 3: From Source

# Clone / download the project
cd nvidia-nim-mcp

# Install dependencies
npm install

# Build TypeScript
npm run build

Option 4: Docker

# Pull from Docker Hub (when published)
docker pull nvidia-nim-mcp

# Or build locally
docker build -t nvidia-nim-mcp .

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🔑 Configuration

Copy .env.example to .env and fill in your API key:

cp .env.example .env

Only NVIDIA_API_KEY is required — all other variables have production-ready defaults:

Variable	Required	Default	Description
NVIDIA_API_KEY	✅	—	Your NVIDIA NGC API key
NVIDIA_NIM_BASE_URL	❌	https://integrate.api.nvidia.com/v1	Base URL for NIM API
DEFAULT_MODEL	❌	black-forest-labs/flux.1-dev	Default model (best image generation)
MAX_REQUESTS_PER_MINUTE	❌	40	Rate limit cap (NVIDIA API limit)
MAX_TOKENS_PER_REQUEST	❌	4096	Hard cap on tokens per request
REQUEST_TIMEOUT_MS	❌	120000	Request timeout (ms)
MAX_RETRIES	❌	3	Max retry attempts on failure
RETRY_DELAY_MS	❌	1000	Base delay between retries (ms)
LOG_LEVEL	❌	info	error|warn|info|debug
ENABLE_IMAGE_GENERATION	❌	true	Enable image generation tools
ENABLE_VISION	❌	true	Enable vision/multimodal tools
ENABLE_MULTIMODAL	❌	true	Enable multimodal task tools

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🚀 Running

NPM Global Installation

# Run the server
nvidia-nim-mcp

# With custom environment variables
NVIDIA_API_KEY=nvapi-your-key LOG_LEVEL=debug nvidia-nim-mcp

NPM Local Installation

# Run with npx
npx nvidia-nim-mcp

# Or add to package.json scripts
# "scripts": { "start": "nvidia-nim-mcp" }
npm start

From Source

# Development mode with auto-reload
npm run dev

# Production mode (compiled)
npm run build && npm start

Docker

# Run with environment variables
docker run --rm \
  -e NVIDIA_API_KEY=nvapi-your-key \
  -e LOG_LEVEL=info \
  nvidia-nim-mcp

# Run in background with port mapping (if needed)
docker run -d \
  --name nvidia-nim-mcp \
  -e NVIDIA_API_KEY=nvapi-your-key \
  nvidia-nim-mcp

Standalone Executable

# Make executable (if not already)
chmod +x dist/index.js

# Run directly
./dist/index.js

# With environment variables
NVIDIA_API_KEY=nvapi-your-key ./dist/index.js

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🔧 MCP Client Configuration

For Global NPM Installation

{
  "mcpServers": {
    "nvidia-nim": {
      "command": "nvidia-nim-mcp",
      "env": {
        "NVIDIA_API_KEY": "nvapi-your-key-here",
        "LOG_LEVEL": "info"
      }
    }
  }
}

For Local NPM Installation

{
  "mcpServers": {
    "nvidia-nim": {
      "command": "npx",
      "args": ["nvidia-nim-mcp"],
      "env": {
        "NVIDIA_API_KEY": "nvapi-your-key-here",
        "LOG_LEVEL": "info"
      }
    }
  }
}

For Direct Executable Path

{
  "mcpServers": {
    "nvidia-nim": {
      "command": "node",
      "args": ["/absolute/path/to/nvidia-nim-mcp/dist/index.js"],
      "env": {
        "NVIDIA_API_KEY": "nvapi-your-key-here",
        "LOG_LEVEL": "info"
      }
    }
  }
}

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🛠️ Available Tools

chat_completion

Multi-turn conversation with any NIM LLM.

{
  "model": "nvidia/nemotron-3-ultra-550b-a55b",
  "messages": [
    { "role": "user", "content": "Explain quantum computing" }
  ],
  "temperature": 0.3,
  "max_tokens": 4096
}

text_generation

Single-prompt text generation (simplified interface).

{
  "prompt": "Write a haiku about machine learning",
  "temperature": 0.5,
  "max_tokens": 512
}

create_embeddings

Convert text(s) to vector embeddings for RAG/search.

{
  "model": "nvidia/nv-embed-v1",
  "input": ["NVIDIA makes GPUs", "AI runs on GPUs"],
  "truncate": "END"
}

rerank_passages

Rerank passages by relevance to a query.

{
  "query": "What is CUDA?",
  "passages": ["CUDA is a GPU programming platform", "NIM serves AI models"],
  "top_k": 3
}

function_calling

Use NIM models with tool/function calling.

{
  "model": "z-ai/glm-5.1",
  "messages": [{ "role": "user", "content": "What's the weather in Paris?" }],
  "tools": [{
    "type": "function",
    "function": {
      "name": "get_weather",
      "description": "Get current weather",
      "parameters": {
        "type": "object",
        "properties": { "city": { "type": "string" } },
        "required": ["city"]
      }
    }
  }]
}

generate_image

Generate images from text prompts using FLUX.1, SDXL, SD3, DiffusionGemma.

{
  "model": "black-forest-labs/flux.1-dev",
  "prompt": "A photorealistic mountain landscape at sunset, 8K",
  "width": 1024,
  "height": 1024,
  "steps": 30,
  "cfg_scale": 3.5,
  "sampler": "euler_a",
  "scheduler": "simple"
}

analyze_image

Analyze and describe images using vision/multimodal models.

{
  "model": "moonshotai/kimi-k2.6",
  "image_url": "https://example.com/image.jpg",
  "prompt": "Describe this image in detail",
  "detail": "high"
}

multimodal_task

Perform multimodal tasks combining text and images.

{
  "model": "minimaxai/minimax-m3",
  "messages": [
    {
      "role": "user",
      "content": [
        { "type": "text", "text": "Analyze this chart" },
        { "type": "image_url", "image_url": { "url": "https://example.com/chart.png" } }
      ]
    }
  ],
  "max_tokens": 2048
}

list_models

List available models with rich metadata and advanced filtering.

{
  "category": "code",
  "commercial_use": true,
  "supports_reasoning": true,
  "tags": ["coding", "agentic"],
  "include_details": true
}

Filter Options:

• category: language, embedding, reranking, vision, code, multimodal, image_generation, all
• commercial_use: Filter by commercial license
• supports_reasoning: Filter by reasoning capability
• supports_vision: Filter by vision capability
• supports_function_calling: Filter by function calling
• supports_multimodal: Filter by multimodal input
• min_context_length: Minimum context window (tokens)
• tags: Filter by use case tags
• hardware: Filter by GPU type (Hopper, Blackwell, Ampere)
• include_details: Include full metadata (benchmarks, image specs, etc.)

get_model_info

Get complete metadata for a specific model.

{ "model_id": "nvidia/nemotron-3-ultra-550b-a55b" }

Returns: licensing, hardware requirements, benchmarks, image gen specs, reasoning modes, tags, supported languages, etc.

compare_models

Compare 2-5 models side-by-side across all decision factors.

{
  "model_ids": [
    "nvidia/nemotron-3-ultra-550b-a55b",
    "deepseek-ai/deepseek-v4-pro",
    "moonshotai/kimi-k2.6",
    "z-ai/glm-5.1"
  ]
}

Returns: Structured comparison table with licensing, hardware, benchmarks, capabilities, tags, image generation specs, etc.

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📦 Supported Models (50+)

Language Models (Frontier Reasoning)

Model	Parameters	Context	License	Commercial	Best For
nvidia/nemotron-3-ultra-550b-a55b	550B (55B active)	131K	OpenMDW-1.1	✅	Frontier reasoning, coding, agentic, 1M context, multilingual
nvidia/nemotron-3-ultra-550b-a55b-instruct	550B	131K	OpenMDW-1.1	✅	Instruction-tuned variant
minimaxai/minimax-m3	428B (22B active)	1M	Non-Commercial	❌	Multimodal, video (30min), 8hr coding, agentic
moonshotai/kimi-k2.6	1T (32B active)	256K	Modified MIT	✅	Long-horizon coding, 300 agents, vision, agentic
deepseek-ai/deepseek-v4-pro	1.6T (49B active)	1M	MIT	✅	Advanced coding, math, reasoning, 3 reasoning modes
z-ai/glm-5.1	754B (DSA)	131K	MIT	✅	Software engineering, agentic, SWE-Bench 58.4%
qwen/qwen3.5-397b-a17b	397B (MoE)	131K	Research	❌	Large-scale multilingual, multimodal
mistralai/mistral-large-3-675b-instruct-2512	675B	131K	Research	❌	Frontier reasoning, multimodal
openai/gpt-oss-120b	120B	131K	Apache 2.0	✅	Open-weight, research, fine-tuning
google/diffusiongemma-26b-a4b-it	25.2B (3.8B active)	256K	Apache 2.0	✅	Diffusion text gen, 35+ langs, fast, multimodal

Code-Specialized Models

Model	Parameters	Context	License	Commercial
z-ai/glm-5.1	754B	131K	MIT	✅
z-ai/glm5	-	128K	Z.ai	✅
qwen/qwen2.5-coder-32b-instruct	32B	131K	Research	❌

Multimodal / Vision Models

Model	Parameters	Context	Vision	Video	License	Commercial
meta/llama-3.2-90b-vision-instruct	90B	128K	✅	❌	Llama 3.2	✅
meta/llama-3.2-11b-vision-instruct	11B	128K	✅	❌	Llama 3.2	✅
nvidia/neva-22b	22B	4K	✅	❌	NVIDIA	✅
microsoft/phi-3.5-vision-instruct	-	128K	✅	❌	MIT	✅
minimaxai/minimax-m3	428B	1M	✅	✅ (30min)	Non-Commercial	❌
moonshotai/kimi-k2.6	1T	256K	✅	✅	Modified MIT	✅

Image Generation Models

Model	Architecture	Resolutions	Aspect Ratios	Max Images	ControlNet	License	Commercial
black-forest-labs/flux.1-dev	Diffusion Transformer	1024², 1152×896, 1344×768, 21:9	1:1, 16:9, 9:16, 4:3, 3:4, 21:9	1	Canny, Depth	Apache 2.0*	❌*
black-forest-labs/flux.1-kontext-dev	Diffusion Transformer	Same	Same	1	-	Apache 2.0*	❌*
nvidia/stable-diffusion-xl	UNet + Attention	1024², 1152×896, 1216×832	1:1, 16:9, 9:16, 4:3, 3:4	4	-	SDXL 1.0	✅**
stabilityai/sd-3-medium	SD3	Same	Same	2	-	Stability AI	✅**
nvidia/sdxl-turbo	ADD	512², 1024²	1:1	4	-	SDXL 1.0	✅**

*Non-commercial default; commercial via contact
**Requires Stability AI membership

Embeddings & Reranking

Model	Type	Context	Dimensions	License	Commercial
nvidia/nv-embedqa-e5-v5	Embedding	512	-	NVIDIA	✅
nvidia/nv-embed-v1	Embedding	4096	-	NVIDIA	✅
baai/bge-m3	Embedding	8192	-	MIT	✅
nvidia/nv-rerankqa-mistral-4b-v3	Reranking	4096	-	NVIDIA	✅

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🏭 Production Checklist

• [x] Environment variable validation on startup
• [x] Exponential backoff retry (configurable)
• [x] Per-minute rate limiter
• [x] Request/response logging with Winston
• [x] Structured JSON logs in production
• [x] Zod input validation for all tools
• [x] Graceful shutdown (SIGINT/SIGTERM)
• [x] Unhandled exception/rejection handlers
• [x] Docker multi-stage build (minimal image)
• [x] Non-root Docker user
• [x] Token cap enforcement
• [x] Single required env var (NVIDIA_API_KEY)
• [x] Feature flags for optional capabilities

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🧪 Testing

The project includes a comprehensive test suite:

• Unit Tests: Configuration, logging, model handling, tool validation
• Integration Tests: All 10 MCP tools with various input scenarios
• Error Handling: Validation of edge cases and failure modes
• Schema Validation: Zod-based input validation for all tools

Running Tests

# Run all tests
npm test

# Run tests with coverage report
npm test -- --coverage

# Run tests in watch mode
npm test -- --watch

# Run specific test file
npm test src/handlers.test.ts

Current Test Status: ✅ All tests passing (96 tests)

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🛠️ Development

Building the Project

# Install dependencies
npm install

# Compile TypeScript to JavaScript
npm run build

# Clean build artifacts
npm run clean

# Development mode with auto-reload
npm run dev

Code Quality

# Run linter
npm run lint

# Run tests
npm test

# Run both linting and tests
npm run check

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🤝 Contributing

Contributions are welcome!

1. Fork the Repository
2. Create a Feature Branch: git checkout -b feature/your-feature-name
3. Make Your Changes: Follow the existing code style and patterns
4. Add Tests: Ensure new functionality is properly tested
5. Run Checks: npm run check to verify code quality and tests
6. Commit Changes: Use clear, descriptive commit messages
7. Push to Your Fork: git push origin feature/your-feature-name
8. Open a Pull Request: Describe your changes and their benefits

Code Standards

• TypeScript: Strict type checking enabled
• ESLint: Code formatting and best practices
• Zod: Runtime validation for all external inputs
• Testing: Comprehensive test coverage for new features
• Documentation: Update README.md for user-facing changes

Development Workflow

1. Setup: Follow the installation instructions
2. Development: Use npm run dev for continuous development
3. Testing: Run npm test to verify your changes
4. Building: Use npm run build to compile the project
5. Linting: Run npm run lint to check code quality

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📦 Packaging & Distribution

NPM Package

• Published to npm registry for easy installation
• Includes compiled JavaScript and TypeScript definitions
• Global and local installation options
• Runs as a standard CLI tool

Docker Image

• Multi-stage build for minimal image size
• Runs as non-root user for security
• Includes health check endpoint
• Easy deployment to containerized environments

Standalone Executable

• Self-contained JavaScript file with shebang
• Can be run directly on any system with Node.js
• No installation required beyond Node.js

Building Packages

# Build the project
npm run build

# Create NPM package (.tgz)
npm pack

# Build Docker image
docker build -t nvidia-nim-mcp .

# All checks (lint, test, build)
npm run check && npm run build

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📄 License

MIT