Aura MCP README

AURA MCP

FHE is the foundation. Aura AI is the application. MCP connects your assistant to encrypted tools.

FHE lets software calculate with data in its encrypted form. Aura's flagship AI application gives that foundation a practical purpose: useful AI with a defined privacy and key-custody model. MCP is the connection from your assistant to the capabilities exposed here.

Aura reports GPT-OSS-20B encrypted inference at 20+ tokens/second on one RTX PRO 6000 Blackwell GPU, with prompt read-in below 2.3 seconds. This is the website's internal benchmark, not a speed measured by this MCP or an independent validation. Benchmark source and scope.

Understand the differences, keys and evidence. Start with the AI application for the product experience; use MCP to explore the supported numeric integration. AI Chat is invitation-only. Request an invitation or arranged demonstration via gen@afhe.io. The public MCP learning demo is open and does not require a Chat invitation.

MCP helps users learn how FHE works, integrate supported encrypted calculations into applications, and explore their potential. Start with a guided public lesson, then map your own use case to its computation, keys and result workflow. The public demo needs installation and host configuration. Confidential application integration also needs local encryption, key custody and authenticated service setup; browser WASM integration is planned. From learning to building.

The AI application is accessed separately; this package does not run model inference. Its executable tools provide encrypted numeric computation. Aura's confirmed FHE database and FHE-AI LLM inference applications are completed and available on request via gen@afhe.io. 中文说明.

Install and connect

These buttons open instructions. Install Node.js 20+, then run these two commands in Terminal (macOS/Linux) or PowerShell (Windows):

npm install -g @aurafhe/mcp@preview
aura-fhe-mcp --config cursor --demo

Use claude or vscode instead of cursor for those apps. Paste the generated settings into your app's MCP configuration and reconnect. Exact steps and paste locations. No Git, npm account, local computation engine or demo key setup is needed. Settings must be generated on the machine that runs the MCP; paths from a cloud workspace do not work on your laptop.

Then ask:

What can I do with Aura? Show me the AI application first, then explain what I can run here.

Your assistant explains the published AI benchmark and the three experiences: the separate AI application, application evaluations, and the MCP integration. Within MCP, choose the optional public-sample lesson or the developer tools. Connecting alone does not start a chat or calculation. In clients with MCP prompts, choose What can I do with Aura? (aura_demo). The overview works without a backend connection; a separate status check or learning lesson checks the service.

This is release 0.5.0-rc.8, an explicitly labelled preview. To pin it, install @aurafhe/mcp@0.5.0-rc.8. To update an existing installation, run the installation command again, regenerate settings and fully restart your client.

What you can run here today

• Add, subtract, multiply and divide encrypted integers and floats.
• Compose sums, products, float averages and weighted sums on the coprocessor.
• Discover available operations, measure request latency and save encrypted results.
• Explore application access and distinguish published benchmarks from live observations.

Numeric arithmetic is a core FHE use case. Binary-operation support is a planned capability milestone; it is not required for numeric computation to be homomorphic. An application uses these building blocks with its own input, key and output workflow. SQL, retrieval and model inference are not current MCP tools. Tool reference.

Try the optional learning lesson

Teach me encrypted computation using Aura's public sample lesson.

The assistant calls aura_start with experience: "learn", explains FHE, prepares encrypted public samples, requests a calculation and saves the encrypted result. It handles identifiers for you. Ask questions or say “one step at a time.” The aura_learn MCP prompt starts this same lesson.

The first example combines public totals 25 and 17. Expected sum: 42. The MCP returns encrypted data; the separate synthetic verifier checks actual numerical results outside the conversation. Follow-up samples 7.5, 2.5 and 2 support an encrypted average. Observed checks and reproduction.

Public-sample demo: Aura manages its demo keys and can decrypt its demo data. Use public examples here. Confidential deployments need a verified key-custody and authorization design. Anything entered in your host assistant is visible to that assistant provider. Security model.

Build with Aura

Describe your application to the assistant using public or invented information. It can help map your task to available operations and explain the client/server configuration you need. Integration steps and the path to simpler setup.

The package contains the connection adapter, not the proprietary computation engine. It accepts no custom plaintext, secret keys or arbitrary file paths in tool arguments. Operator-provisioned encrypted bundles require an authenticated compute-only deployment and separate recipient processing; this path is not Verified mode. The unpublished reference branch is a separate engineering fixture.

Application walkthrough · Investor demo · Architecture · Verification · Security