A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
Star count
2 ★ · metrxbots/mcp-server · read from the GitHub search API · (checked 2026-10-16 20:00:20)
Source
collected from the punkpeye/awesome-mcp-servers list · type: MCP server
Harvest history
first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
Score
score 703 · 2 ★ · 7 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository metrxbots/mcp-server;first catalogued 2026-10-09;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
**Your AI agents are wasting money.** Metrx finds out how much, and fixes it. The official MCP server for Metrx — the AI Agent Cost Intelligence Platform. Give any MCP-compatible agent (Claude, GPT, Gemini, Cursor, Windsurf) the ability to track its own costs, detect waste, optimize model selection, and prove ROI. Why Metrx? | Problem | What Metrx Does | |---------|-----------------| | No visibility into agent spend | Real-time cost dashboards per agent, model, and provider | | Overpaying for LLM calls | Provider arbitrage finds cheaper models for the same task | | Runaway costs | Budget enforcement with auto-pause when limits are hit | | Wasted tokens | Cost leak scanner detects retry storms, context bloat, model mismatch | | Can't prove AI ROI | Revenue attribution links agent actions to business outcomes | Quick Start Try it now — no signup required This starts the server with sample data so you can explore all 23 tools instantly. Connect your real data **Option A — Interactive login (recommended):** Opens your browser to get an API key, validates it, and saves it to ~/.metrxrc so you never need to set env vars. **Option B — Environment variable:** Get your free API key at app.metrxbot.com/sign-up. Add to your MCP client (Claude Desktop, Cursor, Windsurf) If you used --auth, no env block is needed — the key is read from ~/.metrxrc automatically: Or pass the key explicitly via environment: Remote HTTP endpoint For remote agents (no local install needed): From npm 23 Tools Across 10 Domains Dashboard (3 tools) | Tool | Description | |------|-------------| | metrx_get_cost_summary | Comprehensive cost summary — total spend, call counts, error rates, and optimization opportunities | | metrx_list_agents | List all agents with status, category, cost metrics, and health indicators | | metrx_get_agent_detail | Detailed agent info including model, framework, cost breakdown, and performance history | Optimization (4 tools) | Tool | Description | |------|-------------| | metrx_get_optimization_recommendations | AI-powered cost optimization recommendations per agent or fleet-wide | | metrx_apply_optimization | One-click apply an optimization recommendation to an agent | | metrx_route_model | Model routing recommendation for a specific task based on complexity | | metrx_compare_models | Compare LLM model pricing and capabilities across providers | Budgets (3 tools) | Tool | Description | |------|-------------| | metrx_get_budget_status | Current status of all budget configurations with spend vs. limits | | metrx_set_budget | Create or update a budget with hard, soft, or monitor enforcement | | metrx_update_budget_mode | Change enforcement mode of an existing budget or pause/resume it | Alerts (3 tools) | Tool | Description | |------|-------------| | metrx_get_alerts | Active alerts and notifications for your agent fleet | | metrx_acknowledge_alert | Mark one or more alerts as read/acknowledged | | metrx_get_failure_predictions | Predictive failure analysis — identify agents likely to fail before it happens | Experiments (3 tools) | Tool | Description | |------|-------------| | metrx_create_model_experiment | Start an A/B test comparing two LLM models with traffic splitting | | metrx_get_experiment_results | Statistical significance, cost delta, and recommended action | | metrx_stop_experiment | Stop a running model routing experiment and lock in the winner |