NVIDIA NIM Server

License: MIT npm version Bun

A Model Context Protocol (MCP) server that exposes 140+ NVIDIA NIM models to AI agents via stdio.

Supports: chat, embeddings, reranking, vision, image generation (FLUX), OCR, and content safety.

Features

• Thin wrapper around integrate.api.nvidia.com/v1 and ai.api.nvidia.com/v1
• Zero external dependencies (uses global fetch)
• Bun-native, Node 20+ compatible
• Offline model capability snapshot for quick lookups
• Automatic fallback heuristics for unknown models
• Streaming disabled by default (safe for stdio)

Installation

bun add @hallaxius/nvidia-nim-mcp
# or
bunx @hallaxius/nvidia-nim-mcp

Quick Setup

The fastest way to get started — configure your API key once and forget it:

# Configure your key (one-time)
bunx @hallaxius/nvidia-nim-mcp setup "nvapi-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

# Or run interactively (it will prompt for the key)
bunx @hallaxius/nvidia-nim-mcp setup

# Start the MCP server
bunx @hallaxius/nvidia-nim-mcp

The key is saved to ~/.config/nvidia-nim-mcp/config.json. Environment variables still take priority when set.

Usage

1. Set your NVIDIA API Key

You can also configure the key via environment variable:

export NVIDIA_API_KEY=nvapi-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

Supported aliases: NVIDIA_API_KEY, NIM_API_KEY, NVAPI_KEY.

Get your key: https://build.nvidia.com/explore/discover

2. Run the MCP Server

bunx @hallaxius/nvidia-nim-mcp

The server starts over stdio and registers 9 tools.

3. Configure Your AI Agent

See AGENTS.md for integration with:

• Codex CLI
• Claude Desktop
• OpenCode
• Cline
• Continue.dev

Tools

Tool	Description	Default Model
nim_list_models	List all available NIM model IDs	—
nim_get_model_capabilities	Get model type, vision, tools, context	—
nim_chat_completion	Chat completion with LLM	z-ai/glm-5.2
nim_create_embeddings	Generate text embeddings	nvidia/nv-embed-v1
nim_rerank_passages	Rank passages by query relevance	nvidia/llama-3.2-nemoretriever-500m-rerank-v2
nim_vision_inference	Multi-modal inference (text + images)	meta/llama-3.2-90b-vision-instruct
nim_flux_generate_image	Generate images via FLUX	black-forest-labs/flux.1-schnell
nim_ocr_extract	Extract text from images (OCR)	nvidia/nemotron-ocr-v1
nim_safety_classify	Classify text for safety	meta/llama-guard-4-12b

Examples

List Available Models

// Agent code / MCP client usage
const models = await callTool("nim_list_models", {});
// Returns: "z-ai/glm-5.2\nmeta/llama-3.3-70b-instruct\n..."

Chat Completion

const result = await callTool("nim_chat_completion", {
  messages: [{ role: "user", content: "Explain quantum computing simply" }],
  model: "z-ai/glm-5.2",
  temperature: 0.7,
  max_tokens: 2048,
});
// Returns: { content: [{ type: "text", text: "..." }] }

Vision (VLM)

const result = await callTool("nim_vision_inference", {
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "What is in this image?" },
        { type: "image_url", image_url: { url: "data:image/jpeg;base64,/9j/..." } },
      ],
    },
  ],
  model: "meta/llama-3.2-90b-vision-instruct",
});

Embeddings

const result = await callTool("nim_create_embeddings", {
  inputs: ["Hello world", "AI is amazing"],
  model: "nvidia/nv-embed-v1",
});
// Returns: { embeddings: [[...], [...]], model: "nvidia/nv-embed-v1", usage: {...} }

Reranking

const result = await callTool("nim_rerank_passages", {
  query: "What is the capital of France?",
  passages: ["Paris is the capital of France.", "Berlin is the capital of Germany.", "London is the capital of the UK."],
  model: "nvidia/llama-3.2-nemoretriever-500m-rerank-v2",
});
// Returns: { results: [{ index: 0, score: 0.95 }, ...] }

Image Generation (FLUX)

const result = await callTool("nim_flux_generate_image", {
  prompt: "A serene mountain landscape at sunset, digital art",
  width: 1024,
  height: 1024,
  model: "black-forest-labs/flux.1-schnell",
});
// Returns: { content: [{ type: "image", data: "<base64>", mimeType: "image/png" }] }

OCR

const result = await callTool("nim_ocr_extract", {
  image_path: "/absolute/path/to/screenshot.png",
  // or: image_base64: "<base64 string>"
  model: "nvidia/nemotron-ocr-v1",
});
// Returns: { text: "Extracted text...", detections: [{ text, confidence, bounding_box }] }

Safety Classification

const result = await callTool("nim_safety_classify", {
  text: "This is a harmless text.",
  model: "meta/llama-guard-4-12b",
});
// Returns: { safe: true, categories: {...}, explanation: "..." }

Development

Build

bun run build

Type-check

bun run typecheck

Dev mode (stdio server)

bun run dev

Refresh Model Capabilities

bun run refresh

This fetches the latest model catalog from NVIDIA and updates src/models/capabilities.json.

Supported Models

140+ models across families (sourced from build.nvidia.com/models):

Category	Families
LLM	Llama, Nemotron, DeepSeek, Qwen, GLM, Mistral, Mixtral, Gemma, Phi, GPT-OSS, MiniMax, Step, DiffusionGemma, SEED-OSS, Solar, Sarvam M, Stockmark
VLM	Llama Vision, Nemotron VL, Phi-4 Multimodal, Cosmos, PaliGemma, MiniMax-M3, Ising Calibration
Embeddings	NV-Embed, BGE-M3, Llama-Nemotron-Embed, ESM-2 (protein)
Rerank	Llama-Nemotron-Rerank, Rerank-QA-Mistral
Image Gen	FLUX.1 (dev, schnell), FLUX.2 Klein, Stable Diffusion 3.5, Qwen-Image
OCR & Document	Nemotron OCR, Nemoretriever Parse/PAGE, PaddleOCR, NV-YOLOX
Safety	Llama-Guard, Nemotron Safety, Nemoguard, GLiNER PII
Audio & Speech	Whisper, Parakeet, Canary, Magpie TTS, Chatterbox TTS, Riva Translate, Studio Voice, Nemotron Voicechat
Biology & Science	AlphaFold2, ESMFold, OpenFold2/3, Boltz-2, MolMIM, DiffDock, ProteinMPNN, RFDiffusion, Evo2, GenMol, VISTA-3D
Physics & Simulation	CuOpt, FourCastNet, Simcenter STAR-CCM+, Fidelity, Fluent, Spectre-X
Autonomous Driving	BEVFormer, SparseDrive, StreamPETR
3D & Vision	TRELLIS, Relighting, EyeContact, Synthetic Video Detector

The catalog changes — run bun run refresh to update the local capability snapshot, or call nim_list_models at runtime for the latest list.

See https://build.nvidia.com/models for the full catalog.

License

MIT © 2026 @hallaxius