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Nvidia Nim MCP

MCP server for NVIDIA NIM - 50+ LLMs, multimodal, image gen, embeddings, reranking

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Install Nvidia Nim MCP

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "nvidia-nim-mcp": {
      "command": "npx",
      "args": [
        "nvidia-nim-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx nvidia-nim-mcp

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 6d ago.

Overview

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

5/5 checks healthy over the last 36d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 3h ago via OSV.dev · nvidia-nim-mcp (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars1Stargazers on the source repository.
npm downloads582Package downloads in the last 30 days.
Last commit2mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

From the project README

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.

🚀 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

📋 Prerequisites

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

⚙️ Installation

Option 1: NPM Global Installation (Recommended)

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

# Run directly
nvidia-nim-mcp

Option 2: NPM Local Installation

bash
# Initialize your project
npm init -y

# Install locally
npm install nvidia-nim-mcp

# Run with npx
npx nvidia-nim-mcp

Option 3: From Source

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

# Install dependencies
npm install

# Build TypeScript
npm run build

Option 4: Docker

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

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

🔑 Configuration

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

bash
cp .env.example .env

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

VariableRequiredDefaultDescription
NVIDIA_API_KEY✅—Your NVIDIA NGC API key
NVIDIA_NIM_BASE_URL❌https://integrate.api.nvidia.com/v1Base URL for NIM API
DEFAULT_MODEL❌black-forest-labs/flux.1-devDefault model (best image generation)
MAX_REQUESTS_PER_MINUTE❌40Rate limit cap (NVIDIA API limit)
MAX_TOKENS_PER_REQUEST❌4096Hard cap on tokens per request
REQUEST_TIMEOUT_MS❌120000Request timeout (ms)
MAX_RETRIES❌3Max retry attempts on failure
RETRY_DELAY_MS❌1000Base delay between retries (ms)
LOG_LEVEL❌infoerror|warn|info|debug
ENABLE_IMAGE_GENERATION❌trueEnable image generation tools
ENABLE_VISION❌trueEnable vision/multimodal tools
ENABLE_MULTIMODAL❌trueEnable multimodal task tools

🚀 Running

NPM Global Installation

bash
# 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

bash
# Run with npx
npx nvidia-nim-mcp

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

From Source

bash
# Development mode with auto-reload
npm run dev

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

Docker

bash
# 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

bash
# 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

🔧 MCP Client Configuration

For Global NPM Installation

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

For Local NPM Installation

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

For Direct Executable Path

config.json
{
  "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"
      }
    }
  }
}

🛠️ Available Tools

chat_completion

Multi-turn conversation with any NIM LLM.

config.json
{
  "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).

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

create_embeddings

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

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

rerank_passages

Rerank passages by relevance to a query.

config.json
{
  "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.

config.json
{
  "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.

config.json
{
  "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.

config.json
{
  "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.

config.json
{
  "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.

config.json
{
  "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.

config.json
{ "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.

config.json
{
  "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.

📦 Supported Models (50+)

Language Models (Frontier Reasoning)

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Nvidia Nim MCP

nvidia-nim-mcp is an MCP server that connects applications and agents to NVIDIA NIM models through tools for chat completion, text generation, embeddings, reranking, function calling, image generation, image analysis, multimodal tasks, model discovery, and model comparison. It supports more than 50 models and uses TypeScript with Zod input validation.

Install nvidia-nim-mcp globally with npm and run `nvidia-nim-mcp`, or install it locally and run it with `npx nvidia-nim-mcp`. Node.js 18+ is required for npm installation.

nvidia-nim-mcp requires the `NVIDIA_API_KEY` environment variable, using an NVIDIA NGC API key. Optional settings include `NVIDIA_NIM_BASE_URL`, `DEFAULT_MODEL`, rate limits, token limits, request timeouts, retry behavior, logging, and feature flags for image generation, vision, and multimodal tools.

nvidia-nim-mcp supports more than 50 NVIDIA NIM models, including Llama, Nemotron, MiniMax, Kimi, DeepSeek, GLM, Qwen, Mistral, GPT-OSS, DiffusionGemma, FLUX.1, SDXL, and SD3. Capabilities include LLMs, multimodal and vision tasks, image generation, embeddings, reranking, function calling, and code-specialized models.

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