Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Track AI agent costs, detect waste, optimize models, and prove ROI. 23 MCP tools across 10 domains.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
METRX_API_KEYLos valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"metrxbots": {
"command": "npx",
"args": [
"-y",
"@metrxbot/mcp-server"
],
"env": {
"METRX_API_KEY": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add metrxbots --env METRX_API_KEY=your-value -- npx -y @metrxbot/mcp-serverCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"metrxbots": {
"command": "npx",
"args": [
"-y",
"@metrxbot/mcp-server"
],
"env": {
"METRX_API_KEY": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"metrxbots": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@metrxbot/mcp-server"
],
"env": {
"METRX_API_KEY": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub metrxbots, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
Debes completar: METRX_API_KEY
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.2.2) llegó al registro hace 8 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, Microsoft Exchange Online MCP
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Exchange Online admin for MSPs: forwarding audits, EWS posture and offboarding, no PowerShell
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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.
| 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 |
npx @metrxbot/mcp-server --demo
This starts the server with sample data so you can explore all 23 tools instantly.
Option A — 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 — 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.
If you used --auth, no env block is needed — 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" } } } }
For remote agents (no local install needed):
POST https://metrxbot.com/api/mcp
Authorization: Bearer sk_live_your_key_here Content-Type: application/json
npm install @metrxbot/mcp-server
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| Tool | Description |
|---|---|
metrx_run_cost_leak_scan | Comprehensive 7-check cost leak audit across your entire agent fleet |
| 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 — costs vs. attributed outcomes |
metrx_get_attribution_report | Multi-source attribution report with confidence scores and top contributors |
| Tool | Description |
|---|---|
metrx_configure_alert_threshold | Set cost or operational alert thresholds with email, webhook, or auto-pause |
| Tool | Description |
|---|---|
metrx_generate_roi_audit | Board-ready ROI audit report for your AI agent fleet |
| Tool | Description |
|---|---|
metrx_get_upgrade_justification | ROI report for tier upgrades based on current usage patterns |
Pre-built prompt templates for common workflows:
| Prompt | Description |
|---|---|
analyze-costs | Comprehensive cost overview — spend breakdown, top agents, optimization opportunities |
find-savings | Discover optimization opportunities — model downgrades, caching, routing |
cost-leak-scan | Scan for waste patterns — retry storms, oversized contexts, model mismatch |
User: What was my AI cost this week?
→ metrx_get_cost_summary(period_days=7)
Total Spend: $234.56 | Calls: 2,450 | Error Rate: 0.2% ├── customer-support: $156.23 (1,800 calls) └── code-generator: $78.33 (650 calls)
💡 Switch customer-support from GPT-4 to Claude Sonnet: Save $42/week
User: Am I overpaying for my agents?
→ metrx_compare_models(models=["gpt-4o", "claude-3-5-sonnet", "gemini-1.5-pro"])
Model Comparison (per 1M tokens): ├── gpt-4o: $2.50 in / $10.00 out ├── claude-3-5-sonnet: $3.00 in / $15.00 out └── gemini-1.5-pro: $3.50 in / $10.50 out
User: Test Claude 3.5 Sonnet against my GPT-4 setup
→ 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.
This repo also includes @metrxbot/cost-leak-detector — 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.
The server looks for your API key in this order:
METRX_API_KEY environment variable~/.metrxrc file (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 — the key is read from ~/.metrxrc automatically.
| Flag | Description |
|---|---|
--demo | Start with sample data — no API key or signup needed |
--auth | Interactive login — opens browser, validates key, saves to ~/.metrxrc |
--test | Verify your API key and connection |
60 requests per minute per tool. For higher limits, contact support@metrxbot.com.
git clone https://github.com/metrxbots/mcp-server.git
cd mcp-server npm install npm run typecheck npm test
See CONTRIBUTING.md for guidelines.
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 — just a GitHub namespace constraint.
MIT — see LICENSE.
Did Metrx work for you? We'd love to hear it — good or bad.
If you installed but hit a snag, tell us what happened — we read every report.