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.
Recommended Servers
Fast LLM & Agents & MCPs is a comprehensive repository that explores the concepts of Large Language Models (LLMs), Agents, and Model Context Protocols (MCPs) both theoretically and practically. It includes various tools, frameworks, and sample codes for developing intelligent agents and applications using LLMs.
Flomo MCP is a tool designed for writing notes to the Flomo application, allowing users to easily manage and organize their notes.
MCPollinations is a multimodal Model Context Protocol (MCP) server that allows AI assistants to generate images, text, and audio through the Pollinations APIs.
The Deep Research MCP Server is a Model Context Protocol (MCP) compliant server for comprehensive, up-to-date web research. It leverages Tavily's Search & Crawl APIs to gather, aggregate, and structure information for LLM-powered documentation generation.
NPM Helper is a Model Context Protocol (MCP) server designed to assist with NPM package management and dependency updates, enabling LLMs like Claude to interact with npm packages, search the npm registry, and maintain up-to-date dependencies.
AgentMode is an all-in-one Model Context Protocol (MCP) server that connects your coding AI to various databases, data warehouses, data pipelines, and cloud services, streamlining your development workflow.
This project provides a Model Context Protocol (MCP) server for NATS, enabling AI models and applications to interact with NATS messaging systems through a standardized interface. It exposes a comprehensive set of tools for interacting with NATS servers, making it ideal for AI-powered applications that need to work with messaging systems.
Kollektiv MCP enables you to build personal LLM knowledge base in seconds and use it from your favorite editor / client. No more infrastructure setup, chunking, syncing - just upload your data and start chatting. Supports all major MCP clients out of the box - Cursor, Windsurf, Claude Desktop, etc.
Podbean MCP Server is a management tool that connects any MCP-compatible AI assistant to the Podbean API, allowing users to manage their podcasts, episodes, and analytics through natural conversation.