PrivAgents MCP Server

Overview

PrivAgents is a privacy‑first MCP (Model Context Protocol) server that bridges the gap between on‑device and cloud AI services by enabling secure, homomorphically encrypted data processing. In environments where sensitive information—such as medical records, financial details, or personal preferences—must remain confidential, traditional cloud models pose a trust risk while on‑device models lack the computational heft of large language models. PrivAgents resolves this tension by allowing user data to be encrypted locally, sent over a secure channel to the MCP server for similarity calculations or other analytics, and returned encrypted for local decryption. This end‑to‑end encryption pipeline ensures that raw data never leaves the user’s device, even while leveraging powerful cloud inference.

The server exposes a modular MCP interface that can be consumed by any agent compliant with the protocol. It supports two primary agents: an Ollama Agent for local reasoning and an OpenAI Agent that taps into cloud APIs. Developers can choose the agent type that best matches their compute and privacy constraints, or even mix both within a single workflow. The MCP server’s core capability is performing similarity calculations on encrypted vectors using homomorphic encryption libraries such as TenSEAL, enabling recommendation or matching tasks without revealing the underlying vectors.

Key features include:

• Homomorphic Encryption (HE) support: All payloads are encrypted on the device, processed in ciphertext form, and decrypted locally.
• Modular architecture: The MCP server can be extended with additional encrypted processing routines or integrated into existing pipelines.
• Agent‑agnostic interface: Any agent that implements the MCP contract can interact with the server, promoting interoperability.
• End‑to‑end confidentiality: The data flow—from input to result—remains encrypted throughout, satisfying stringent privacy regulations.

Real‑world scenarios that benefit from PrivAgents are abundant. In healthcare, patient symptoms can be encrypted and sent to a server that computes similarity against medical knowledge bases without exposing the patient's condition. In finance, encrypted transaction vectors can be matched against fraud patterns while preserving client confidentiality. Personalization services—such as movie or product recommendation systems—can deliver tailored suggestions by computing similarity on encrypted preference vectors, ensuring that user tastes are never exposed in plain text.

Integrating PrivAgents into an AI workflow is straightforward: the agent handles encryption, sends a context payload to the MCP server via the standard MCP protocol, receives an encrypted response, and decrypts it for local consumption. Because the server operates purely on ciphertext, developers can deploy it in trusted environments or even in distributed setups without compromising user privacy. The result is a robust, privacy‑preserving AI ecosystem that leverages the strengths of both on‑device and cloud models while keeping sensitive data secure.