Deeppowers
DEEPPOWERS is a Fully Homomorphic Encryption (FHE) framework built for MCP (Model Context Protocol), aiming to provide end-to-end privacy protection and high-efficiency computation for the upstream and downstream ecosystem of the MCP protocol.
What is Deeppowers?
Deeppowers is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to deeppowers is a fully homomorphic encryption (fhe) framework built for mcp (model context protocol), aiming to provide end-to-end privacy protection and high-efficiency computation for the upstream an...
DEEPPOWERS is a Fully Homomorphic Encryption (FHE) framework built for MCP (Model Context Protocol), aiming to provide end-to-end privacy protection and high-efficiency computation for the upstream and downstream ecosystem of the MCP protocol.
This server falls under the Security category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- DEEPPOWERS is a Fully Homomorphic Encryption (FHE) framework
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx deeppowersConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Deeppowers
DeepPowers is a Fully Homomorphic Encryption (FHE) framework built for the MCP ecosystem that aims to provide end-to-end privacy protection for AI computation, allowing data to remain encrypted while being processed by LLMs and MCP servers. Built on the Concrete-ML library by Zama, it supports FHE-compatible machine learning tasks such as linear models, decision trees, and logistic regression, and targets integration with providers including DeepSeek, GPT, Gemini, and Claude. Security researchers and enterprises that need to ensure sensitive data is never exposed in plaintext during AI inference pipelines will find this framework relevant as it matures.
Prerequisites
- C++ build toolchain (gcc or clang) for compiling the FHE components
- Python 3.9 or higher (for Concrete-ML integration)
- Git for cloning the repository and the companion demo repository
- Familiarity with Fully Homomorphic Encryption concepts
- An MCP-compatible client such as Claude Desktop for integration testing
Clone the DeepPowers repository
Clone the main DeepPowers repository to your local machine.
git clone https://github.com/deeppowers/deeppowers.git
cd deeppowersReview the documentation and demo repository
DeepPowers is under active development. Start by reading the user guide and cloning the demo repository to understand the current capabilities.
# Read the user guide
cat docs/userguide.md
# Clone the demo repository
git clone https://github.com/deeppowers/Deeppowers-Demo.gitInstall Concrete-ML dependency
DeepPowers builds on Zama's Concrete-ML library. Install it in a Python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
pip install concrete-mlBuild the C++ components
Follow the build instructions in the repository to compile the FHE acceleration components. Refer to the CMakeLists.txt or Makefile in the repository for the correct build commands.
cmake -B build -S .
cmake --build buildConfigure MCP integration
Once the server is built and running, add it to your Claude Desktop MCP configuration. Refer to the demo repository for the current recommended command and arguments as the project evolves.
{
"mcpServers": {
"deeppowers": {
"command": "python",
"args": ["-m", "deeppowers"]
}
}
}Deeppowers Examples
Client configuration
Placeholder MCP configuration for DeepPowers. Consult the official demo repository for the current authoritative command and arguments as the project is under active development.
{
"mcpServers": {
"deeppowers": {
"command": "python",
"args": ["-m", "deeppowers"]
}
}
}Prompts to try
Conceptual prompts for exploring FHE-protected AI computation once the server is running.
- "Run a logistic regression inference on this encrypted dataset without decrypting the inputs."
- "Demonstrate that this data remains homomorphically encrypted throughout the MCP tool call pipeline."
- "Use the DeepPowers FHE layer to process this sensitive medical record without exposing plaintext."
- "Show the performance overhead of FHE computation versus plaintext for this linear model."
- "Explain which Concrete-ML model types are currently supported by DeepPowers FHE inference."Troubleshooting Deeppowers
CMake build fails with missing C++ compiler or dependency errors.
Ensure a full C++ build toolchain is installed. On Ubuntu/Debian run `sudo apt install build-essential cmake`. On macOS install Xcode Command Line Tools with `xcode-select --install`.
Concrete-ML installation fails or is incompatible with the Python version.
Concrete-ML requires Python 3.9–3.11. Run `python3 --version` to verify. Create a fresh virtual environment with a supported Python version and retry `pip install concrete-ml`.
MCP server does not start or exposes no tools.
DeepPowers is under active development and the MCP server interface may not yet be fully implemented. Check the GitHub repository for the latest release notes, open issues, and the Deeppowers-Demo repository for current working examples.
Frequently Asked Questions about Deeppowers
What is Deeppowers?
Deeppowers is a Model Context Protocol (MCP) server that deeppowers is a fully homomorphic encryption (fhe) framework built for mcp (model context protocol), aiming to provide end-to-end privacy protection and high-efficiency computation for the upstream and downstream ecosystem of the mcp protocol. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Deeppowers?
Follow the installation instructions on the Deeppowers GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Deeppowers?
Deeppowers works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Deeppowers free to use?
Yes, Deeppowers is open source and available under the Apache-2.0 license. You can use it freely in both personal and commercial projects.
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