Eunomia MCP Server – A Governance‑First Bridge for AI Workflows

Eunomia MCP Server extends the open‑source Eunomia data‑governance framework into the Model Context Protocol ecosystem. It turns any MCP‑compatible assistant into a policy‑aware conduit, allowing developers to apply robust data‑protection rules—such as PII detection or user access control—to text streams that flow through LLM pipelines. By orchestrating multiple external MCP servers, Eunomia MCP Server removes the friction of manually inserting governance checks into each service.

The server exposes a simple “Orchestra” configuration: a collection of instruments that define how incoming data should be examined or transformed. When an assistant receives a request, Eunomia MCP Server intercepts the payload, runs it through the configured instruments (e.g., to redact email addresses or for domain‑specific filtering), and then forwards the sanitized text to downstream MCP servers. This guarantees that every piece of content adheres to organizational compliance policies before it reaches the model or any external system.

Key capabilities include:

• Policy enforcement at the edge: Apply regex‑based or ML‑driven instruments to any text field in real time, ensuring that sensitive information never leaves the controlled environment.
• Multi‑server orchestration: Connect to several MCP services (web browsers, database adapters, custom tools) and route data through them in a predefined sequence.
• Extensibility: New instruments can be added to the Orchestra, and custom MCP servers can be integrated with minimal boilerplate.
• Transparent logging: Each transformation step is recorded, providing audit trails essential for regulatory compliance.

Typical use cases span a wide range of AI applications. A customer‑support bot can strip personally identifiable data from user queries before passing them to a language model, while a content‑generation tool can enforce brand guidelines by filtering out disallowed terminology. In data‑science pipelines, Eunomia MCP Server can sanitize raw logs before they are ingested into analytics models, preventing inadvertent leakage of protected data.

For developers building AI workflows, the server offers a plug‑and‑play layer that sits between the assistant and any external service. By leveraging MCP’s request/response model, Eunomia MCP Server can be dropped into existing infrastructures with little change to client code. Its declarative configuration aligns well with modern DevOps practices, enabling versioned policy management alongside model deployments.

In summary, Eunomia MCP Server delivers a policy‑first, highly modular bridge that protects data integrity and compliance across AI ecosystems, all while preserving the flexibility that developers expect from MCP‑based toolchains.