Zio Llm Proxy – MCP Server Overview

The Zio LLM Proxy bridges OpenAI’s function‑calling capable chat models with any local MCP (Model Context Protocol) server, adding a layer of privacy‑first data handling. It intercepts user queries, forwards them to the LLM enriched with a dynamic tool list that reflects the configured MCP servers, and then relays the model’s tool requests back to the underlying data sources. By performing PII (Personally Identifiable Information) checks on any retrieved content, the proxy ensures that sensitive data is never exposed to the model without explicit user consent. This workflow protects privacy while still enabling powerful, data‑driven AI interactions.

The proxy solves a common pain point for developers: seamlessly integrating local data stores with cloud‑based LLMs while maintaining strict privacy controls. In many scenarios, organizations cannot expose their data to external services due to regulatory or security requirements. Zio LLM Proxy allows the LLM to query local MCP servers—such as a filesystem or database server—without ever sending raw data over the network. The PII module, built on regular‑expression detection for English text, flags potentially sensitive information and prompts the user to approve or deny its inclusion in the LLM prompt. This gives developers a transparent, auditable decision point that satisfies compliance mandates.

Key capabilities include:

• Dynamic tool injection: The proxy supplies the LLM with a real‑time list of available MCP servers and their tool signatures via function calling, ensuring the model can invoke only supported actions.
• Stateful conversation handling: While this design is not horizontally scalable, it maintains in‑memory context for each user session, simplifying state management during the chat flow.
• PII detection and consent workflow: Sensitive data is identified before reaching the LLM, and users are asked whether they wish to share it. If denied, the conversation is safely terminated.
• Error handling for context limits: When retrieved data exceeds the model’s token window, the proxy reports an error and stops the dialog to avoid incomplete or corrupted responses.

Typical use cases span compliance‑heavy industries: a legal firm querying local case files, a healthcare provider accessing patient records for AI triage, or an enterprise data analyst exploring proprietary datasets with GPT‑style assistance—all without risking accidental data leakage. The proxy integrates into existing AI pipelines by acting as a middleware layer; developers simply point their OpenAI SDK at the proxy’s endpoint, and the rest is handled automatically.

Unique advantages of Zio LLM Proxy include its lightweight Docker deployment, minimal configuration (just an API key and shared directory path), and the ability to plug in any number of MCP servers through a simple configuration file. Although it lacks authentication and horizontal scaling, its focus on privacy, ease of integration, and transparent PII handling make it a compelling choice for developers who need to marry local data access with powerful LLM capabilities.