Nvidia Nim MCP

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "nvidia-nim-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "npx"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

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:

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

🚀 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 →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Nvidia Nim MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "nvidia-nim-mcp": { "command": "npx", "args": ["-y", "nvidia-nim-mcp"] } }

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Technical Specs & Signals

CategoryKnowledge & Memory

36Quality signal: Fair · 36/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools16/30

Adoption & activity1/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Nvidia Nim MCP →Install in Claude Desktop Install in Cursor Install in VS Code

Nvidia Nim MCP

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "nvidia-nim-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "npx"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

NVIDIA NIM MCP Server

🚀 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:

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

🚀 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 →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Nvidia Nim MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "nvidia-nim-mcp": { "command": "npx", "args": ["-y", "nvidia-nim-mcp"] } }

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Technical Specs & Signals

CategoryKnowledge & Memory

36Quality signal: Fair · 36/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools16/30

Adoption & activity1/15

Community engagement0/10

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Nvidia Nim MCP →Install in Claude Desktop Install in Cursor Install in VS Code