Nvidia Nim MCP

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:

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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)

Code-Specialized Models

Multimodal / Vision Models

Image Generation Models

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

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