Side-by-side comparison of two Model Context Protocol servers — install paths, tools, quality signals, and directory engagement so you can pick the right one for Claude, Cursor, and other MCP clients.
Query Langfuse traces, debug exceptions, analyze sessions, and manage prompts. Full observability toolkit for LLM applications.
Query your Spanlens LLM observability from any MCP client. 7 read tools for request logs, agent traces, cost stats, anomalies, model-savings, and per-user analytics across OpenAI, Anthropic, and Gemini. Open source, self-hostable. npx -y @spanlens/mcp-server
Quality signal
47/100 (Fair)
45/100 (Fair)
Install path
uvx · high
npx · high
Engagement
3 0 0 102
1 0 0 11
Tools
Query and inspect Langfuse traces and observationsFind and triage exceptions and error countsAnalyze sessions and user activity detailsManage prompts, datasets, annotation queues, and scoresIncludes an agent skill with ready-made debugging playbooksSupports local deployment with token and output control
Request logging with full prompt, response, cost, and latency dataAgent tracing for multi-step and tool-based LLM callsCost tracking and model usage analytics across providersAnomaly detection and PII scanningPrompt versioning and A/B experiment supportSelf-hostable with a single Docker command and open source MIT license
