The Problem: AI Agents Are Expensive and Opaque
Every time you spin up an AI agent — whether it's a coding assistant, a customer support bot, or a data pipeline processor — you're burning through API credits, compute time, and token budgets. The problem is that most agents have zero visibility into their own costs.
You deploy them, they run, and at the end of the month you get a surprise bill. Wouldn't it be better if your agents could track their own spending in real-time, enforce budgets, and even generate revenue?
Enter the Agent Cost Tracker MCP Server
The Agent Cost Tracker MCP is an open-source MCP (Model Context Protocol) server that gives your AI agents real-time cost awareness. It integrates directly with Stripe to provide:
- Real-time cost tracking — Every LLM call, API request, and compute operation is logged with its cost
- Per-agent budgets — Set monthly or per-task spending limits
- Usage analytics — See exactly where your money is going
- Stripe billing integration — Bill clients, track revenue, and monetize your agents
- Alert thresholds — Get notified when costs exceed configurable limits
How It Works
The server implements the MCP protocol, meaning any MCP-compatible client (Claude Desktop, Cline, Cursor, custom agents) can connect to it and start tracking costs immediately.
# Example: Connecting your agent to the cost tracker
from mcp import ClientSession
async with ClientSession(transport) as session:
# Track a model call
cost = await session.call_tool("track_cost", {
"agent_id": "code-reviewer-1",
"model": "gpt-4o",
"input_tokens": 1500,
"output_tokens": 420,
"cost_per_million_input": 2.50,
"cost_per_million_output": 10.00
})
print(f"This call cost: ${cost:.4f}")
# Get current month's spending
report = await session.call_tool("get_spending_report", {
"agent_id": "code-reviewer-1",
"period": "month"
})
print(f"Monthly spend: ${report['total']:.2f}")
Real-World Use Cases
1. Client-Facing Agent Billing
Run agents for clients and bill them accurately. The Stripe integration creates invoices automatically based on actual usage. No more estimating — every token is tracked.
2. Internal Cost Allocation
Different departments or projects get charged for the agents they use. Set hard caps so one team can't blow the entire budget.
3. Agent Marketplace
Build agents that are self-sustaining — they pay for their own API costs by generating value (or direct revenue through Stripe checkout).
Setting Up Your Own Cost Tracker
Getting started takes about 5 minutes:
# Clone the repo
git clone https://github.com/Rumblingb/agent-cost-tracker-mcp.git
cd agent-cost-tracker-mcp
# Install dependencies
pip install -r requirements.txt
# Configure your Stripe keys
cp .env.example .env
# Add your STRIPE_SECRET_KEY and STRIPE_WEBHOOK_SECRET
# Start the server
python server.py
Then configure your MCP client to connect to it. You'll immediately have full visibility into every cost your agents generate.
Monetization: Let Your Agents Pay Their Way
The most exciting feature is the Stripe integration. You can:
- Create subscription plans for your agent services
- Generate payment links for one-time usage
- Automatically invoice recurring agent usage
- Track revenue alongside costs for true P&L per agent
👉 Subscribe to Agent Cost Tracker Pro — $19/month
Architecture Overview
┌──────────────┐ ┌──────────────────────┐ ┌───────────┐
│ MCP Client │────▶│ Cost Tracker MCP │────▶│ Stripe │
│ (Your Agent)│ │ (Python Server) │ │ API │
└──────────────┘ └──────────────────────┘ └───────────┘
│
┌─────▼──────┐
│ SQLite │
│ (Cost DB) │
└────────────┘
The server stores all cost data locally in SQLite (or PostgreSQL for production), and syncs billing events to Stripe.
Get Started Today
The Agent Cost Tracker MCP is free and open-source. Star it on GitHub, contribute, and start building agents that understand the value of every API call they make.
🔗 GitHub: https://github.com/Rumblingb/agent-cost-tracker-mcp
💳 Subscribe to Pro ($19/month): https://buy.stripe.com/dRm4gB7Tpck43ry9351oI0x
Built with ❤️ by the AgentPay team. We believe AI agents should be financially autonomous.
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