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.
MCP-native agent evaluation and observability server with trace logging, output quality evaluation, cost tracking, 12 built-in eval rules, real-time dashboard, and PII detection.
AI API cost tracking and budget enforcement across 11 LLM providers. 6 tools for spend analytics, budget monitoring, session summaries, and key management.
Quality signal
48/100 (Fair)
45/100 (Fair)
Install path
npx · high
npx · high
Engagement
0 0 0 7
1 0 0 16
Tools
Hierarchical span trace logging with latency, token usage, and cost13 built-in evaluation rules including PII and prompt injection detectionCost aggregation and budget threshold alertsReal-time dark-mode web dashboard for trace and evaluation visualizationSQLite storage for instant querying of trace dataCustom evaluation rules via Zod schemas
Proxy API gateway logging requests with token counts and dollar costsBudget enforcement rejecting requests exceeding limits before provider callSDK support for Python and TypeScript with local cost estimation optionCLI tool wrapping commands to intercept and report costs without code changesIntegration handlers for LangChain, LlamaIndex, and Pydantic AIGitHub Action for CI cost capping and PR comment reporting