Best AI Observability Tools for Model Context Protocol (MCP)

Scorable is an innovative platform utilizing AI for evaluation and monitoring, specifically crafted to assist developers in assessing, regulating, and enhancing the performance of applications developed with large language models. The platform empowers teams to construct personalized automated evaluators, often termed AI "judges," which evaluate the responses of AI systems to users and determine if the outputs align with established quality metrics such as accuracy, relevance, helpfulness, tone, and adherence to policies. Developers can articulate their measurement objectives in straightforward language, and Scorable then creates a customized evaluation framework that tests AI outputs against specific contextual criteria, moving beyond standard benchmarks. These evaluators can be seamlessly integrated into the application's code, enabling continuous oversight of AI systems, including chatbots, retrieval-augmented generation (RAG) systems, or autonomous agents, even while they are functioning in live production settings. This capability ensures that developers maintain high standards for AI performance over time and can swiftly adapt to evolving requirements.

telemetry.dev serves as an observability platform that is fully integrated with OpenTelemetry, specifically designed for AI agents and applications involving large language models. It enables developers to monitor various elements such as model calls, tool steps, retrievals, logs, and metrics, while also allowing them to analyze issues relating to failures, latency, token consumption, and projected expenses. Additionally, users can draw comparisons across different models, providers, and environments to gain deeper insights into performance. The platform features native support for TypeScript and Python SDKs, along with integrations for various providers and frameworks, as well as standard OTLP/HTTP ingestion methods. To enhance data privacy and security, it incorporates environment-specific capture controls, SDK masking, and server-side redaction, ensuring that teams can effectively manage the sensitive content associated with prompts, responses, and telemetry data. This comprehensive functionality enables organizations to optimize their AI applications and ensure robust observability across their operations.