MCP server that predicts your monthly LLM spend from local Claude Code / Cursor / Codex logs. Forecasts end-of-month $, ranks spike drivers, warns
Most monitoring and observability work still happens through a UI a human drives. Milo MCP server moves it into the conversation instead. MCP server that predicts your monthly LLM spend from local Claude Code / Cursor / Codex logs. Forecasts end-of-month $, ranks spike drivers, warns before budget breach. Free tier + paid tier ($19/mo), MIT licensed.
Install it in Claude Code, Cursor, Continue, or any MCP-aware editor. Three free tools, one paid tool, zero phone-home.
Installation goes through your MCP client rather than a global install: point it at milo-usage-forecaster on PyPI and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Configuration is passed through the environment: MILO_USAGE_FORECASTER_PRO_KEY, MILO_USAGE_FORECASTER_HMAC_KEY, HMAC_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Among the monitoring and observability options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes.
This entry was verified against Milo's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
{
"mcpServers": {
"milo-usage-forecaster": {
"command": "mcp-usage-forecaster",
"env": {
"MILO_USAGE_FORECASTER_PRO_KEY": ""
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| MILO_USAGE_FORECASTER_PRO_KEY | Credential the server authenticates with. | Yes |
| MILO_USAGE_FORECASTER_HMAC_KEY | Credential the server authenticates with. | Yes |
| HMAC_KEY | Credential the server authenticates with. | Yes |
Give your coding agent the full DevTools toolbox: traces, network, console, heap snapshots and Lighthouse.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.
Errors with full context — stack traces, issue triage and AI-powered root-cause analysis from Sentry's server.
Enables enhanced web research capabilities for large language models through intelligent search queuing and advanced content extraction.
Automates browser interactions and enables Large Language Models (LLMs) to interact with web pages through Playwright and Chrome DevTools Protocol
Guides tool usage by providing recommendations for MCP tools at each problem-solving stage.