Patronus MCP Server

Patronus is a robust machine‑learning framework that offers a suite of tools for evaluating, optimizing, and experimenting with large language models. The Patronus MCP Server exposes this functionality through the Model Context Protocol, giving AI assistants a standardized way to invoke complex evaluation pipelines and run experiments without needing direct access to the underlying SDK. This solves a common pain point for developers: integrating sophisticated LLM assessment workflows into conversational agents while keeping the agent’s codebase lightweight and secure.

The server offers a clean, declarative interface for three core activities: initialization, evaluation, and experimentation. By simply providing an API key, project name, and optional application identifier, a client can bootstrap the Patronus environment in seconds. Once initialized, the assistant can request single or batch evaluations against a wide array of built‑in evaluators such as lynx (hallucination detection) or judge (conciseness scoring). Each evaluation can be customized with criteria, explanation strategies, and contextual data, allowing fine‑grained control over how the model’s output is judged. For batch runs, multiple evaluators are applied in parallel, returning a consolidated JSON report that is easy to parse and display within the assistant’s UI.

Beyond evaluation, the server supports experiments that run across datasets. Developers can define custom evaluator functions—leveraging Patronus’s adapter pattern—to compare model outputs against ground truth or to enforce domain‑specific rules. The MCP interface accepts these custom evaluators as part of an experiment request, orchestrating the entire pipeline from data ingestion to metric aggregation. This makes it straightforward for AI assistants to trigger reproducible research workflows, track performance over time, and surface actionable insights directly to end users.

Integration into an AI workflow is seamless. An assistant simply calls the MCP tool “initialize” to establish context, then uses “evaluate”, “batch_evaluate”, or “run_experiment” as needed. The server returns structured JSON, which the assistant can render in natural language or visual dashboards. Because all heavy lifting is performed server‑side, the client remains stateless and lightweight, reducing latency and eliminating the need for local LLM dependencies. This architecture is particularly valuable in cloud‑hosted or edge deployments where resource constraints and security concerns limit what can run locally.

Unique advantages of the Patronus MCP Server include its extensibility—developers can plug in new evaluators or adapters without modifying the core protocol—and its scalable evaluation engine that handles parallel processing of large datasets. The clear separation between initialization, single‑task evaluation, and full experiments also encourages modular design: an assistant can perform quick sanity checks with a single evaluator or launch a full benchmark suite, all through the same protocol. For teams building AI‑powered products that require rigorous model validation, this server provides a turnkey solution that blends flexibility with operational simplicity.