# @hallaxius/nvidia-nim-mcp

License: MIT npm version Bun Tests code style

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)
TypeScript strict mode with BiomeJS linting
Test suite with 23+ tests (bun test)

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

Integration with:

OpenCode: A global @nvidia subagent is available at ~/.config/opencode/agents/nvidia.md for querying NIM models and capabilities.

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

Setup

bun install

Build

bun run build

Type-check

bun run typecheck

Lint & Format

bun run lint

Uses BiomeJS for linting and formatting. Runs automatically before publish via prepare.

Test

bun test

23+ tests covering capabilities, config, HTTP client, and server structure.

Dev mode (stdio server)

bun run dev

Model Capabilities

Capabilities are inferred in real-time using model ID heuristics. No static snapshot needed — the model list is fetched live from the NVIDIA API via nim_list_models.

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 — call nim_list_models at runtime for the latest list.

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

NVIDIA NIM MCP Server