# gpu-bridge/mcp-server [Health: Active]

**Category:** 🔗 Aggregators  
**Repository:** https://github.com/gpu-bridge/mcp-server  
**GitHub Stars:** 2  
**npm Downloads (last month):** 243  
**Views:** 3  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/gpu-bridge-mcp-server

## Description
Unified GPU inference API with 30 AI services (LLM, image gen, video, TTS, whisper, embeddings, reranking, OCR) as MCP tools. Pay-per-use via x402 USDC or API key credits.

## Tools
Capabilities this server exposes over MCP:

- **gpu_run** — Run any GPU-Bridge AI service. 30 services available: LLM inference (sub-second), image generation (FLUX, SD3.5), video generation, video enhancement (up to 4K), speech-to-text (Whisper, <1s), TTS (40+ voices), music generation, voice cloning, embeddings, document reranking (Jina), OCR, PDF/document parsing, NSFW detection, image captioning, visual Q&A, background removal, face restoration, upscaling, stickers, and more. Use gpu_catalog to see all available services.
- **gpu_catalog** — List all available GPU-Bridge services with pricing and model info. No authentication required.
- **gpu_status** — Check the status of a GPU-Bridge job and retrieve results.
- **gpu_balance** — Check GPU-Bridge credit balance, daily spend, volume discount tier, and job history.
- **gpu_estimate** — Estimate the cost of a GPU-Bridge service before running it. No authentication required.

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `npx` (confidence: high):

```json
"mcpServers": {
  "mcp-server": {
    "command": "npx",
    "args": ["-y","@gpu-bridge/mcp-server"],
    "env": {
      "GPUBRIDGE_API_KEY": ""
    }
  }
}
```

**Requires environment variables:** `GPUBRIDGE_API_KEY` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for).

## Documentation

## What the gpu-bridge/mcp-server MCP server does

gpu-bridge/mcp-server MCP server connects MCP-compatible clients to GPU-Bridge's catalog of 30 hosted AI services. The catalog includes language-model inference, code generation, image generation and editing, video generation and enhancement, speech transcription, text-to-speech, music and sound-effect generation, embeddings, document reranking, OCR, PDF parsing, image analysis, background removal, and content moderation.

The main execution tool accepts a service identifier, service-specific input, and an optional priority selection. Available priorities are `fast` for lower latency or `cheap` for lower cost. The service catalog and cost estimator can be used before submitting an inference request, which helps an agent select a model and estimate usage.

## How it works

The gpu-bridge/mcp-server MCP server exposes five tools:

- `gpu_run` submits work to a selected GPU-Bridge service.
- `gpu_catalog` lists available services, models, capabilities, and pricing.
- `gpu_estimate` estimates the cost of a service request without authentication.
- `gpu_status` checks a job and retrieves its result.
- `gpu_balance` reports credit balance, daily spending, volume-discount tier, and job history.

GPU-Bridge supports two payment paths. A client can send an API key, or an autonomous agent can use x402 to pay per request with USDC on Base L2. With x402, the provider returns an HTTP 402 payment response, the agent submits payment, and the request is retried with payment proof. The README describes this flow for direct API use; the MCP configuration example uses an API key.

## Setup and configuration

Install the package from npm and register it as a local stdio server. For Claude Desktop, add an entry under `mcpServers` and restart the application:

```json
{
  "mcpServers": {
    "gpu-bridge": {
      "command": "npx",
      "args": ["-y", "@gpu-bridge/mcp-server"],
      "env": {
        "GPUBRIDGE_API_KEY": "your_api_key_here"
      }
    }
  }
}
```

The API key is obtained from GPU-Bridge. The README also documents a keyless x402 option for agents that can make USDC payments, but it does not provide a separate MCP configuration example for that mode. Claude Desktop configuration paths are listed for both macOS and Windows.

## Tools and capabilities

Use `gpu_catalog` to inspect current service keys rather than assuming a model is available. Examples in the catalog include `llm-4090`, `flux-schnell`, `whisper-l4`, `tts-l4`, `embed-l4`, `rerank`, `animatediff`, `video-enhance`, and `pdf-parse`.

The catalog covers sub-second LLM options, image generation, multilingual OCR, visual question answering, speaker diarization, voice cloning TTS, music generation, code embeddings, document reranking, and video upscaling up to 4K. Actual inputs vary by service, so callers should consult the service information before constructing a `gpu_run` request.

## Limitations and notes

Inference is pay-per-use rather than described as a fully local operation. Listed starting prices include separate rates for language-model tokens, images, transcription minutes, speech characters, embeddings, reranking queries, and document parsing. Prices and service availability should be checked through `gpu_catalog` or GPU-Bridge's published catalog.

The README explicitly shows Claude Desktop and states that other MCP-compatible AI clients can use the server, but it does not document client-specific setup for Cursor, Windsurf, or Cline. The server's listed tools cover GPU-Bridge services; they do not expose arbitrary local GPU workloads.

_Full upstream README: https://allmcps.com/mcp/gpu-bridge-mcp-server/readme_