emublog2mlv-mcp

emublog2mlv-mcp

Converts ECU Master EMU Black binary logs to MegaLogViewer HD format. Allows AI assistants to inspect channel summaries and perform analysis.

Category
Visit Server

README

emublog2mlv

Convert ECU Master EMU Black .emublog binary logs into a format MegaLogViewer HD can open — a MegaSquirt .msl (tab-delimited) or plain .csv.

CI Release License: MIT

The EMU Client stores logs in an undocumented gzip-compressed binary format that only its own software can open. This tool decodes that format directly, so you can analyse EMU logs in MegaLogViewer HD (overlay channels, histograms, VE/AFR analysis) instead of being locked into the EMU Client viewer.


Three ways to use it

1. Standalone apps (no Python needed) — easiest

Download from the Releases page:

  • emublog2mlv-gui.exe — double-click for a simple window: add one or many logs (or a whole folder), choose .msl/.csv, set the output folder or a custom name for a single file, then Convert. Best for batch/mass conversion.

  • emublog2mlv.exe — command-line version. Drag a log onto it, or:

    emublog2mlv.exe yourlog.emublog       REM -> yourlog.msl
    emublog2mlv.exe *.emublog              REM batch convert
    

Open the resulting .msl in MegaLogViewer HD.

2. Python package (CLI + library)

pip install git+https://github.com/zazzn/emublog2mlv
emublog2mlv yourlog.emublog              # -> yourlog.msl
emublog2mlv yourlog.emublog --csv        # -> yourlog.csv
emublog2mlv *.emublog                     # batch convert
emublog2mlv yourlog.emublog --summary     # print channel ranges as JSON (no file written)

As a library:

from emublog2mlv import convert, summarize
convert("yourlog.emublog", fmt="msl")     # returns stats dict
summarize("yourlog.emublog")              # per-channel min/max/mean

3. MCP server (let an AI use it as a tool)

emublog2mlv ships an MCP server so an AI assistant (Claude Desktop / Claude Code, etc.) can convert and inspect logs for you.

pip install "emublog2mlv[mcp] @ git+https://github.com/zazzn/emublog2mlv"
claude mcp add emublog2mlv -- emublog2mlv-mcp

Tools exposed:

Tool What it does
convert_emublog(input_path, output_format="msl", output_path=None) Convert a log; returns stats
emublog_summary(input_path) Per-channel min/max/mean/first/last (no file written)
list_channels() The decoded channel map (offsets/scales)

Then just ask: "Convert C:\logs\drive.emublog and tell me where lambda goes lean."


Output

A .msl file has a name row, a units row, then one row per 25 Hz sample, with a Time column in seconds. Time is anchored to the device's frameStamp clock, so it stays accurate even though the real sample rate (~25.18 Hz) jitters — a naïve index / 25 drifts ~0.7% over a few minutes.

Decoded channels

31 measured channels plus 2 derived (AFR, AFRTarget = λ × 14.7):

RPM, MAP, TPS, VE, IgnitionAngle, IgnFromTable, InjectorsPW, InjectorsDC, InjCalTime, ShortTermTrim, Lambda, LambdaTarget, IAT, ChargeTemp, CLT, OilTemp, OilPressure, FuelPressure, IATCorrection, Ethanol, AirFlow, CAM1valveDC, AccelEnrichment, AccelEnrichmentPW, KnockSensor, BatteryVoltage, BoostTarget, BoostDC, BoostDCFromTable, IdleTarget, FCustomCorrActive.

Still unmapped — each needs a drive that exercises it: knock level / ignition retard (a knock event), decel enrichment (overrun), warmup/afterstart (a cold start), boost PID correction (closed-loop boost), plus dwell, BARO, gear, vehicle speed, AFR2/Lambda2, and engine power/torque — they read flat in a gentle cruise. EGT is omitted (this car has no EGT sensor). See Contributing.


How the format was reverse-engineered

ECU Master does not publish the .emublog layout. It was recovered by correlation:

  1. The file is gzip-compressed. Decompressed, it is a 12-byte header followed by fixed 308-byte records, one per sample (records = (size − 12) / 308).
  2. Export the same drive from EMU Client to CSV (it caps at 13 channels per export, so several exports are taken). These CSVs are row-aligned to the binary (sample N = record N).
  3. For each known CSV channel, Pearson-correlate its series against a u16 (or u8) value read at every byte offset; the offset with |r| ≈ 1.0 is the channel, and the linear fit gives the scale.
  4. Overlapping clusters (e.g. oil-pressure / oil-temp / fuel-pressure / coolant packed into bytes 111–115) were pinned by dumping raw bytes next to the CSV truth — temperatures turned out to be single-byte integers, pressures a dominant high byte.

Every decoded channel matches the EMU CSV export to the decimal. (The only discrepancies are ~0.8% of rows where EMU's own CSV export jitters ±1–2 frames against the binary — so this converter is actually more faithful than re-exporting CSV from the EMU Client.)

This was reverse-engineered for EMU Black V2 (firmware 2.169). EMU Black V3 writes a different .emublog3 layout, which is not supported.


Contributing: add a channel

The channel map lives in src/emublog2mlv/core.py as the CHANNELS table: (name, unit, offset, kind, scale, extra). To map a new channel, get a log that exercises it (e.g. a boost pull for boost/knock/EGT), export it to CSV from EMU Client, correlate each byte offset against the CSV column, verify the decode against the CSV, then add a row. PRs welcome.

Run the tests:

pip install -e ".[dev]"
pytest -q

License

MIT © 2026 Eric W

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured