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Official LangChain MCP server for LangSmith providing conversation history, tracing, datasets, experiments, and billing usage access with comprehensive observability for AI applications.

Unified MCP server for querying OpenTelemetry traces across multiple backends (Jaeger, Tempo, Traceloop), enabling AI agents to analyze distributed traces for automated debugging and observability with support for OTel semantic conventions.

Provide observability and tracing for debugging AI agents with AgentOps API. This MCP server enables developers to monitor, trace, and debug their AI agent applications through natural language interfaces.

Inspect traces, manage prompts, curate datasets, and run experiments using Arize Phoenix, an open-source AI and LLM observability tool. Provides comprehensive monitoring capabilities for AI applications.

MCP server bringing real-time production context such as logs, metrics, and traces into local environments to auto-fix code faster using observability data.

MCP server providing access to OpenTelemetry traces and metrics through Logfire, enabling AI agents to investigate observability data and performance issues.

MCP implementation for Opik (by Comet ML) enabling seamless IDE integration and unified access to prompts, projects, traces, and metrics for LLM observability.

Observability platform MCP server for Honeycomb, enabling AI agents to query telemetry data, analyze distributed traces, and debug production systems through natural language with OpenTelemetry integration.