Metrx — Scorecard for Your AI Workforce

# Metrx > Metrx is the scorecard for your AI workforce. Track what every AI agent costs, identify waste, optimize model selection, and prove ROI — in under 60 seconds via MCP server. Free tier, no credit card required. ## What Metrx Does - Tracks per-agent, per-model LLM costs in real-time (OpenAI, Anthropic, Google, Cohere, Mistral) - Generates cost optimization recommendations (model switching, token reduction, provider arbitrage) - Runs A/B experiments comparing models with statistical significance testing - Detects cost leaks: idle agents, model overprovisioning, missing caching, retry storms - Links agent actions to business outcomes (revenue attribution, ROI calculation) - Provides budget governance with hard/soft spending limits and auto-pause enforcement - Publishes anonymized LLM cost benchmarks from the Metrx network ## What Metrx Does NOT Do - Metrx is NOT an LLM gateway or proxy — it observes and analyzes, it does not route API traffic - Metrx is NOT a prompt management tool — use Langfuse or PromptLayer for prompt versioning - Metrx is NOT an agent hosting platform — it works with your existing infrastructure - Metrx does NOT store prompt/completion content — only metadata, cost signals, and performance metrics ## For AI Agents (MCP Server) Install (stdio): `npx @metrxbot/mcp-server` Remote (HTTP): `https://metrxbot.com/api/mcp` 23 tools across 10 domains: ### Cost Tracking - `metrx_get_cost_summary` — Get fleet-wide cost summary with spend, call counts, error rates, agent breakdown - `metrx_list_agents` — List all agents with status, category, cost metrics - `metrx_get_agent_detail` — Get detailed cost history and performance for one agent ### Optimization - `metrx_get_optimization_recommendations` — AI-powered savings recommendations (model switch, token guardrails, arbitrage) - `metrx_apply_optimization` — Apply one-click optimization fix to an agent - `metrx_route_model` — Get optimal model recommendation based on tas