Passive Cost Memory for Agent Development
@RaleighSF
About 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
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"burnwatch": {
"command": "node",
"args": [
"node_modules/burnwatch/dist/mcp-server.js"
]
}
}
}Tools
No tools detected
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
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.
More AI & Agents MCP servers
LinkedIn MCP Server
stickerdanielOpen-source MCP server for LinkedIn. Give Claude and any MCP-compatible AI agent access to profiles, companies, jobs, and messages.
Sequential Thinking Multi-Agent System (MAS)
FradSerAn advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP.
1Panel
1Panel-dev🔥 1Panel is a modern, open-source VPS control panel — and the only one with native AI agent support. Run Ollama models, deploy OpenClaw agents, and manage your entire server stack from one clean web interface.
🔎 GPT Researcher
assafelovicAn autonomous agent that conducts deep research on any data using any LLM providers
Perplexity Ask MCP Server
ppl-aiThe official MCP server implementation for the Perplexity API Platform
Comments