Atla MCP Server

The Atla MCP Server bridges the gap between AI assistants and the Atla evaluation platform, giving developers a ready‑made conduit to benchmark large language models (LLMs) against state‑of‑the‑art evaluation criteria. By exposing Atla’s scoring engine as an MCP tool, the server lets agents ask for objective feedback on a model’s output without leaving their native workflow. This removes the need to manually craft HTTP requests, handle authentication, or parse raw JSON responses—everything is wrapped in a simple, declarative function call.

At its core, the server offers two evaluation tools:

• scores a single response against a specified criterion and returns both a numeric score and a textual critique.
• extends this to a list of criteria, delivering a separate score and critique for each.

These tools are powered by Atla’s proprietary evaluation models, which have been trained on extensive human‑rated datasets. Consequently, the feedback is not only quantitative but also contextually rich, providing actionable insights into strengths and weaknesses such as factual accuracy, coherence, or adherence to style guidelines.

For developers building AI‑driven applications, the server’s value lies in its seamless integration with popular MCP clients—OpenAI Agents SDK, Claude Desktop, and Cursor. Once configured, an assistant can invoke evaluation tools as if they were native capabilities, enabling workflows like continuous quality monitoring in production pipelines or interactive debugging during model development. The server also handles authentication via an API key, ensuring secure access to Atla’s resources without exposing credentials in client code.

Unique advantages of the Atla MCP Server include its standardized interface (conforming to MCP specifications), which guarantees compatibility across different assistants, and its dual‑mode evaluation (single vs. multiple criteria), giving teams flexibility to choose the depth of analysis they need. By centralizing evaluation logic, it reduces duplication and fosters reproducible benchmarking across projects.

In practice, teams can use the server to automatically score outputs from a newly fine‑tuned model before deployment, compare performance across multiple LLMs in A/B tests, or provide real‑time feedback to users interacting with a chatbot. Whether you’re building a compliance‑aware customer support bot or conducting research on emergent behavior, the Atla MCP Server equips you with a robust, easy‑to‑use evaluation layer that scales with your AI ecosystem.