About
Multi-agent billing and cost allocation tracking
Security Report
This MCP server for cost attribution and budget management is well-structured with appropriate permissions for its purpose. No critical vulnerabilities or malicious patterns detected. Minor code quality concerns around input validation and error handling are present but do not significantly impact security. The server relies on a legitimate SQLite database for state management and includes reasonable scoping of tool operations. Supply chain analysis found 4 known vulnerabilities in dependencies (0 critical, 3 high severity). Package verification found 1 issue.
4 files analyzed · 9 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-mdfifty50-boop-agent-costcenter": {
"args": [
"-y",
"agent-costcenter-mcp"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
agent-costcenter-mcp
MCP server for per-agent cost attribution and budget management. Answer "which agent costs the most?" across your entire AI agent fleet.
Features
- Per-agent cost tracking — register agents, log LLM and tool calls, get per-agent spend
- Built-in pricing table — 11 models (Claude, GPT, Gemini, DeepSeek) with fuzzy name matching
- Budget management — set caps and alerts, check remaining budget, warn or block on overspend
- Cost reports — breakdown by agent, team, project, or all; includes model-level detail and percentages
- Anomaly detection — automatically flag agents with 2x+ spending spikes
- Model comparison — compare cost-per-1K-tokens across all models used
Quick Start
npx agent-costcenter-mcp
Claude Desktop
{
"mcpServers": {
"agent-costcenter": {
"command": "npx",
"args": ["agent-costcenter-mcp"]
}
}
}
Tools
| Tool | Description |
|---|---|
register_agent | Register agent with team, project, model, and budget cap |
log_llm_call | Log an LLM call — auto-calculates cost from pricing table |
log_tool_call | Log a tool/API call cost |
get_cost_report | Cost breakdown by agent, team, project, or all |
set_budget_alert | Set spending threshold (warn or block) |
check_budget | Check if agent is within budget |
get_cost_anomalies | Find agents with unusual spending spikes |
compare_models | Compare cost efficiency across models |
Resources
| URI | Description |
|---|---|
costcenter://summary | Current total spend across all agents |
costcenter://pricing | Built-in model pricing table |
Built-in Pricing (per 1M tokens)
| Model | Input | Output |
|---|---|---|
| claude-opus-4 | $15.00 | $75.00 |
| claude-sonnet-4 | $3.00 | $15.00 |
| claude-haiku-4 | $0.80 | $4.00 |
| gpt-4o | $2.50 | $10.00 |
| gpt-4o-mini | $0.15 | $0.60 |
| gpt-4.1 | $2.00 | $8.00 |
| gpt-4.1-mini | $0.40 | $1.60 |
| gemini-2.5-pro | $1.25 | $10.00 |
| gemini-2.5-flash | $0.15 | $0.60 |
| deepseek-v3 | $0.27 | $1.10 |
| deepseek-r1 | $0.55 | $2.19 |
Model names are fuzzy-matched (case-insensitive, strips version suffixes).
Example Workflow
1. register_agent(agent_id="researcher", team="discovery", model="claude-sonnet-4", budget_cap_usd=10)
2. log_llm_call(agent_id="researcher", model="claude-sonnet-4", input_tokens=5000, output_tokens=2000)
3. log_tool_call(agent_id="researcher", tool_name="web_search", duration_ms=1200, cost_usd=0.01)
4. check_budget(agent_id="researcher")
5. get_cost_report(scope="all")
6. get_cost_anomalies()
7. compare_models()
Development
npm install
npm test
npm run dev # watch mode
License
MIT
Reviews
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