Atla MCP Server Overview

Atla MCP Server bridges the gap between large language models and advanced evaluation services by exposing Atla’s flagship Selene 1 evaluation suite through the Model Context Protocol (MCP). It solves a common pain point for AI developers: the difficulty of integrating sophisticated, domain‑specific metrics into conversational agents. By offering a standardized MCP interface, the server lets assistants—whether built with OpenAI Agents, Claude Desktop, or Cursor—request real‑time quality assessments without having to write custom API wrappers.

The server’s core capability is evaluation. A model can submit a single response or an entire batch to Selene 1, which returns detailed scores across multiple metrics such as cliche detection, helpfulness, or factual consistency. The MCP implementation also exposes a catalog of available metrics and allows clients to create new ones or retrieve existing definitions by name. This flexibility means that teams can tailor the evaluation pipeline to their own quality standards while still benefiting from Selene 1’s state‑of‑the‑art scoring algorithms.

Key features include:

• Real‑time single response evaluation for interactive agents that need instant feedback.
• Batch processing to evaluate large corpora or test sets efficiently, ideal for training and fine‑tuning workflows.
• Dynamic metric management: list, create, or fetch metrics via MCP calls, enabling custom evaluation strategies.
• Seamless integration with popular agent frameworks—OpenAI Agents, Claude Desktop, and Cursor—all of which can discover the server through their MCP configuration files.

Typical use cases span from interactive creative writing (e.g., a poet assistant that refines its output based on cliche scores) to customer support automation (where helpfulness and accuracy are scored in real time). In research settings, the batch evaluation feature supports large‑scale benchmarking of new models against established metrics. Because the server operates over MCP, developers can swap in alternative evaluation backends or extend the metric set without modifying agent code.

In summary, Atla MCP Server turns Selene 1 into a pluggable evaluation service that fits naturally into modern AI toolchains. Its standardized MCP interface, combined with robust metric handling and batch support, gives developers a powerful yet straightforward way to enforce quality standards across diverse AI applications.