NVIDIA NIM MCP Server
Enables AI agents to access 140+ NVIDIA NIM models for chat, embeddings, reranking, vision, image generation, OCR, and content safety via stdio.
README
@hallaxius/nvidia-nim-mcp
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/v1andai.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:
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 refreshto update the local capability snapshot, or callnim_list_modelsat runtime for the latest list.
See https://build.nvidia.com/models for the full catalog.
License
MIT © 2026 @hallaxius
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