Passive Cost Memory for Agent Development
@RaleighSF
关于 Passive Cost Memory for Agent Development
Burnwatch detects every paid service in your project, tracks spend across 14 AI-native tools (Anthropic, OpenAI, Vercel, Supabase, Stripe, Scrapfly, Browserbase, and more), and exposes real-time cost data via MCP tools — so any LLM can factor budget into its recommendations. Four
基本信息
配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"burnwatch": {
"command": "node",
"args": [
"node_modules/burnwatch/dist/mcp-server.js"
]
}
}
}工具
未检测到工具
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
概览
What is Passive Cost Memory for Agent Development?
Passive Cost Memory for Agent Development (burnwatch) is a cost monitoring tool for agent projects that provides spend overviews, service deep dives, and paid-service scans across 14 supported services like Anthropic, OpenAI, and AWS.
How to use Passive Cost Memory for Agent Development?
Install via npm install burnwatch, then add a configuration entry to your MCP client with the command node and args ["node_modules/burnwatch/dist/mcp-server.js"].
Key features of Passive Cost Memory for Agent Development
- Full project spend overview with budget alerts
- Deep dive on any service with gotchas and alternatives
- Scan a project for all paid services
- Browse a registry of 14 supported services
- Confidence badges (LIVE, CALC, EST, BLIND) indicate data quality
Use cases of Passive Cost Memory for Agent Development
- Monitor total project spending and catch budget overruns early
- Analyze cost breakdown for individual services to optimize spending
- Discover all paid services used across a project
- Get alternatives and gotchas for each supported service
FAQ from Passive Cost Memory for Agent Development
What services does it support?
Anthropic, OpenAI, Vercel, Supabase, Stripe, Scrapfly, Browserbase, Upstash, Resend, Inngest, PostHog, Google Gemini, Voyage AI, and AWS, with more coming.
How are spend figures categorized?
Each spend figure has a confidence badge: LIVE (real billing API data), CALC (fixed monthly cost, projected), EST (estimated from usage patterns), or BLIND (detected but not yet tracked).
How do I install it?
Run npm install burnwatch and add the MCP server configuration to your client as shown in the README.
What tools does it provide?
Four tools: get_spend_brief, get_service_spend, detect_paid_services, and list_registry_services.
How do I scan a project for paid services?
Use the detect_paid_services tool to scan a project and identify all paid services it uses.
AI 与智能体 分类下的更多 MCP 服务器
MCP Server - Remote MacOs Use
baryhuangThe only general AI agent that does NOT requires extra API key, giving you full control on your local and remote MacOs from Claude Desktop App
Solon Ai
opensolonJava AI application development framework (supports LLM-tool,skill; RAG; MCP; Agent-ReAct,Team-Agent). Compatible with java8 ~ java25. It can also be embedded in SpringBoot, jFinal, Vert.x, Quarkus, and other frameworks.
meGPT - upload an author's content into an LLM
adriancoCode to process many kinds of content by an author into an MCP server
MCP-NixOS - Because Your AI Assistant Shouldn't Hallucinate About Packages
utensilsMCP-NixOS - Model Context Protocol Server for NixOS resources
Gemini MCP Server
aliargunMCP server implementation for Google's Gemini API
评论