Burnmeter MCP Server

burnmeter

burnmeter analyses Claude Code token spend from the session logs already on your machine. It reads the JSONL session files, adds up the token counts in every usage record, and reports two views: how the total splits across output, input, cache-creation, and cache-read (the efficiency view), and the full totals with a per-model split (the breakdown view). One binary serves a command-line interface and a stdio MCP server.

Everything is local. There is no network access, no API key, and no account. The log directory is taken from CONTEXTBURN_LOG_DIR when set and defaults to ~/.claude/projects; every *.jsonl file under it is read recursively.

Install

npx @reneturcios/burnmeter breakdown

Or run the container image:

docker run -i ghcr.io/cloudcorprecords/burnmeter

The container defaults to the MCP server (see below). Pass CLI arguments to use it as a CLI instead. Note the container reads logs from inside the container, so mount your log directory and point CONTEXTBURN_LOG_DIR at the mount if you use it for real logs.

CLI

burnmeter efficiency
burnmeter breakdown
burnmeter breakdown --log-dir ~/somewhere/else

burnmeter --version

Both reports print JSON. --log-dir wins over CONTEXTBURN_LOG_DIR, which wins over the ~/.claude/projects default. Exit codes: 0 on success, 1 when a report fails, 2 for usage errors.

A breakdown contains logDir, filesScanned, entriesWithUsage, malformedLines, totals (the four token counts plus total_tokens), and byModel (the same counts per model, with an entry count, sorted by total tokens descending). An efficiency report contains the same scan fields and totals, plus shares: each category as a percentage of total tokens, rounded to two decimal places.

How entries are counted: a log line that is not valid JSON (or not a JSON object) is skipped and counted in malformedLines. An entry counts when it carries a usage object, either at the top level or under its message; the four counts input_tokens, output_tokens, cache_creation_input_tokens, and cache_read_input_tokens are summed, with a missing or non-numeric field counting as 0. The model is message.model, falling back to a top-level model, falling back to unknown. Entries without a usage object are ignored.

When the directory is missing or no usage entries are found, the report is not an error and no numbers are invented: totals and shares are zero, and the report carries a note field saying no usage entries were found and where it looked.

MCP server

One bin serves both faces. The mcp subcommand starts a stdio MCP server:

npx -y @reneturcios/burnmeter mcp

MCP client configuration:

{
  "mcpServers": {
    "burnmeter": {
      "command": "npx",
      "args": ["-y", "@reneturcios/burnmeter", "mcp"]
    }
  }
}

To read logs from a non-default directory, add CONTEXTBURN_LOG_DIR to the client's env for this server.

Tools

Both tools are parameterless, as documented in the original tool's public spec.

Development

npm install
npm run build   # tsc -> dist/
npm test        # node test runner over test/*.test.ts, no network (temporary fixture logs only)
npm run dev     # run the CLI from source via tsx

License

MIT — see LICENSE.