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 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
TypeScript MCP server for the Langfuse Public API. 27 read tools covering traces, observations, sessions, scores, score-configs, prompts (with version/label), datasets, dataset items, dataset runs, metrics, models, projects, comments, media, and health. Distributed as npx -y langfuse-mcp with provenance-signed releases.
Quality signal
45/100 (Fair)
49/100 (Fair)
Install path
npx · high
npx · high
Engagement
0 0 0 11
2 0 0 1
Tools
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
27 read-only tools covering telemetry and monitoring dataSupports filtering and fetching by IDs for traces, sessions, scores, prompts, datasetsAccess to models, projects, comments, media metadataCustom metrics query execution with JSON inputHealth check endpoint for credential validation
