io.github.metrxbots/mcp-server
Track AI agent costs, detect waste, optimize models, and prove ROI. 23 MCP tools.
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Metrx MCP Server
Your AI agents are wasting money. Metrx finds out how much, and fixes it.
The official MCP server for Metrx β 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 β no signup required
npx @metrxbot/mcp-server --demo
This starts the server with sample data so you can explore all 23 tools instantly.
Connect your real data
Option A β Interactive login (recommended):
npx @metrxbot/mcp-server --auth
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 β Environment variable:
METRX_API_KEY=sk_live_your_key_here npx @metrxbot/mcp-server --test
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 β the key is read from ~/.metrxrc automatically:
{
"mcpServers": {
"metrx": {
"command": "npx",
"args": ["@metrxbot/mcp-server"]
}
}
}
Or pass the key explicitly via environment:
{
"mcpServers": {
"metrx": {
"command": "npx",
"args": ["@metrxbot/mcp-server"],
"env": {
"METRX_API_KEY": "sk_live_your_key_here"
}
}
}
}
Remote HTTP endpoint
For remote agents (no local install needed):
POST https://metrxbot.com/api/mcp
Authorization: Bearer sk_live_your_key_here
Content-Type: application/json
From npm
npm install @metrxbot/mcp-server
23 Tools Across 10 Domains
Dashboard (3 tools)
| Tool | Description |
|---|---|
metrx_get_cost_summary | Comprehensive cost summary β 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 β 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 |
Cost Leak Detector (1 tool)
| Tool | Description |
|---|---|
metrx_run_cost_leak_scan | Comprehensive 7-check cost leak audit across your entire agent fleet |
Attribution (3 tools)
| Tool | Description |
|---|---|
metrx_attribute_task | Link agent actions to business outcomes for ROI tracking |
metrx_get_task_roi | Calculate return on investment for an agent β costs vs. attributed outcomes |
metrx_get_attribution_report | Multi-source attribution report with confidence scores and top contributors |
Alert Configuration (1 tool)
| Tool | Description |
|---|---|
metrx_configure_alert_threshold | Set cost or operational alert thresholds with email, webhook, or auto-pause |
ROI Audit (1 tool)
| Tool | Description |
|---|---|
metrx_generate_roi_audit | Board-ready ROI audit report for your AI agent fleet |
Upgrade Justification (1 tool)
| Tool | Description |
|---|---|
metrx_get_upgrade_justification | ROI report for tier upgrades based on current usage patterns |
Prompts
Pre-built prompt templates for common workflows:
| Prompt | Description |
|---|---|
analyze-costs | Comprehensive cost overview β spend breakdown, top agents, optimization opportunities |
find-savings | Discover optimization opportunities β model downgrades, caching, routing |
cost-leak-scan | Scan for waste patterns β retry storms, oversized contexts, model mismatch |
Examples
"How much am I spending?"
User: What was my AI cost this week?
β metrx_get_cost_summary(period_days=7)
Total Spend: $234.56 | Calls: 2,450 | Error Rate: 0.2%
βββ customer-support: $156.23 (1,800 calls)
βββ code-generator: $78.33 (650 calls)
π‘ Switch customer-support from GPT-4 to Claude Sonnet: Save $42/week
"Find me savings"
User: Am I overpaying for my agents?
β metrx_compare_models(models=["gpt-4o", "claude-3-5-sonnet", "gemini-1.5-pro"])
Model Comparison (per 1M tokens):
βββ gpt-4o: $2.50 in / $10.00 out
βββ claude-3-5-sonnet: $3.00 in / $15.00 out
βββ gemini-1.5-pro: $3.50 in / $10.50 out
"Test a cheaper model"
User: Test Claude 3.5 Sonnet against my GPT-4 setup
β metrx_create_model_experiment(agent_id="agent_123",
model_a="gpt-4o", model_b="claude-3-5-sonnet-20241022", traffic_split=10)
Experiment started: 90% GPT-4o, 10% Claude 3.5 Sonnet
Check back in 14 days for statistical significance.
Companion Tool: Cost Leak Detector
This repo also includes @metrxbot/cost-leak-detector β a free, offline CLI that scans your LLM API logs for wasted spend. No signup, no cloud, no data leaves your machine.
npx @metrxbot/cost-leak-detector demo
It runs 7 checks (idle agents, premium model overuse, missing caching, high error rates, context overflow, no budgets, arbitrage opportunities) and gives you a scored report in seconds. See the full docs.
Configuration
API Key (required)
The server looks for your API key in this order:
METRX_API_KEYenvironment variable~/.metrxrcfile (created by--auth)
Run npx @metrxbot/mcp-server --auth to save your key, or set the env var directly.
| Variable | Required | Description |
|---|---|---|
METRX_API_KEY | Yes* | Your Metrx API key (get one free) |
METRX_API_URL | No | Override API base URL (default: https://metrxbot.com/api/v1) |
*Not required if you've run --auth β the key is read from ~/.metrxrc automatically.
CLI Flags
| Flag | Description |
|---|---|
--demo | Start with sample data β no API key or signup needed |
--auth | Interactive login β opens browser, validates key, saves to ~/.metrxrc |
--test | Verify your API key and connection |
Rate Limiting
60 requests per minute per tool. For higher limits, contact support@metrxbot.com.
Development
git clone https://github.com/metrxbots/mcp-server.git
cd mcp-server
npm install
npm run typecheck
npm test
Contributing
See CONTRIBUTING.md for guidelines.
Links
- Website: metrxbot.com
- Docs: docs.metrxbot.com
- npm: @metrxbot/mcp-server
- Smithery: metrxbot/mcp-server
- Support: support@metrxbot.com
A Note on Naming
The product is Metrx (metrxbot.com). The npm scope is @metrxbot and the Smithery listing is metrxbot/mcp-server. The GitHub organization is metrxbots (with an s) because metrxbot was already taken on GitHub. If you see metrxbot vs metrxbots across platforms, they're the same project β just a GitHub namespace constraint.
License
MIT β see LICENSE.
π¬ Feedback
Did Metrx work for you? We'd love to hear it β good or bad.
- GitHub Discussions: Start a thread β questions, ideas, what you're building
- Bug reports: Open an issue
- Quick feedback: Drop a comment on our Product Hunt listing
If you installed but hit a snag, tell us what happened β we read every report.
