Arize Phoenix

Arize Phoenix is a Model Context Protocol (MCP) server that bridges the gap between AI assistants and production‑grade machine‑learning monitoring. It turns Arize’s powerful model‑performance observability platform into a first‑class MCP resource, allowing Claude and other AI agents to query real‑time metrics, retrieve historical performance data, and trigger alerts without leaving the conversation. For developers, this means a single, conversational interface to assess drift, bias, and accuracy across multiple models, all while staying within the workflow of an AI assistant.

The server exposes a rich set of capabilities that mirror Arize’s core API. Developers can list available models, fetch model‑specific metrics such as precision, recall, and latency, and drill down into per‑instance predictions. In addition to read operations, Phoenix supports write actions like logging new inference data or updating model metadata, enabling end‑to‑end observability pipelines to be managed through natural language commands. The MCP implementation is designed for low latency, ensuring that monitoring queries return in milliseconds so they can be used in real‑time decision making.

Key features include:

• Unified metric access – retrieve any Arize metric via a simple prompt, from overall accuracy to feature‑level drift scores.
• Historical trend analysis – request time‑series data and generate visual summaries directly within the assistant.
• Alert integration – trigger or suppress alerts, and even create new monitoring rules on the fly.
• Secure data handling – all requests are authenticated against Arize’s OAuth scopes, keeping sensitive model data protected.

Real‑world use cases abound. A DevOps engineer can ask the assistant to “show me the latest latency trend for model X” and immediately see a plotted chart. A data scientist can request “has there been any drift in feature Y over the past week?” and receive a concise explanation, saving hours of manual querying. In production environments, the assistant can monitor multiple models concurrently and surface anomalies before they affect downstream services.

By embedding observability directly into conversational AI, Arize Phoenix gives developers a powerful, low‑friction tool for model stewardship. It eliminates the need to juggle dashboards and scripts, allowing teams to focus on building better models while ensuring they remain reliable, compliant, and performant.