Atla MCP Server
Description
The Atla MCP Server provides a standardized interface for LLMs to interact with the Atla API for state-of-the-art LLMJ evaluation.
Features
- LLM-as-a-Judge-Atla's evaluator models assess AI outputs based on criteria like relevance, correctness, helpfulness, and logical coherence. These models provide scores and actionable critiques to help developers improve their AI applications.
- Selene Evaluator Models-The flagship model, Selene, offers high-performance evaluations. It supports context windows up to 32,000 tokens and can be customized to fit specific evaluation needs.
- Customizable Evaluation Metrics-Developers can define custom evaluation metrics or use Atla's pre-built ones, such as relevance, correctness, and faithfulness. This flexibility allows for tailored assessments aligned with specific application goals.
- Integration into Development Pipelines-Atla can be integrated into continuous integration (CI) pipelines, enabling automated evaluations during development. This helps in catching regressions early and maintaining consistent performance.
- Real-Time Monitoring and Guardrails-The platform offers live monitoring capabilities to detect performance drifts and implement guardrails, ensuring the AI applications remain reliable in production environments.
- Easy Integration and SDK Support-With a RESTful API and a Python SDK, Atla facilitates easy integration into various development environments, allowing developers to start evaluations with minimal setup.
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