Huawei National University of Singapore The Model Context Protocol (MCP) enables AI-powered tools to interact with autonomous agents and Large Language Models (LLMs), but currently processes user inputs and inference results in plaintext. This could introduce some privacy risks and violates the principle of least privilege when processing user data. This contribution specifies a ciphertext-based AI inference extension for MCP. The design leverages Homomorphic Encryption (HE) to allow an MCP server or remote tool to perform inference directly on encrypted data without accessing the plaintext. The server receives encrypted payloads, computes over ciphertexts using an evaluation key, and returns an encrypted result that only the MCP client can decrypt. To ensure interoperability across various network environments, this document defines a JSON-RPC message format independent of the underlying transport layer (supporting both local stdio and remote Server-Sent Events). The format extends the existing MCP 'tools/ call' method with fields for encrypted payloads, algorithm identifiers, and key references. Additionally, this document discusses security considerations, key management, and implementation trade-offs.