Query robotics, drone, and IoT data in plain English through an MCP server, with an intelligent edge data reduction pipeline that keeps only the windows that matter.
# Add to your Claude Code skills
git clone https://github.com/Extelligence-ai/bagelGuides for using mcp servers skills like bagel.
Last scanned: 8/13/2026
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}bagel is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Extelligence-ai. Query robotics, drone, and IoT data in plain English through an MCP server, with an intelligent edge data reduction pipeline that keeps only the windows that matter. It has 390 GitHub stars.
Yes. bagel passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/Extelligence-ai/bagel" and add it to your Claude Code skills directory (see the Installation section above).
bagel is primarily written in Python. It is open-source under Extelligence-ai on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh bagel against similar tools.
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Unlike cat videos, most data are trapped in the physical world. Bagel unlocks them and lets you chat with your physical data—just like you do with ChatGPT. For example:
Is my IMU sensor overheating?
Can’t wait to try it out? 👉 Check out the Quickstart.
| Industry | Formats |
|---|---|
| Robotics | ROS1, ROS2, MCAP (any profile), ROS text logs (~/.ros/log) |
| Drones | PX4, ArduPilot, Betaflight |
| Automotive | ASAM MDF4 (.mf4), CAN captures (.blf/.asc + DBC) — beta |
| IoT | MQTT (live, Sparkplug B), PostgreSQL / TimescaleDB, InfluxDB 3 |
You already have ros2 *, PlotJuggler, and grep. Bagel doesn't replace them — it
answers the questions they make you work for, then hands off to them:
| You do this today | Ask Bagel instead |
|---|---|
ros2 bag info for metadata |
"Summarize this bag" — same prompt works on PX4, ArduPilot, MCAP, MQTT, Postgres |
ros2 topic echo /imu and eyeball raw values |
"What's the peak z-deceleration in /imu? Running average over 5 s?" — real SQL underneath: peaks, running averages, percentiles, cross-topic correlations |
| Scrub PlotJuggler timelines hunting for the event | "Find every deceleration under −10 m/s² and cut ±30 s snippets" — then open the result in PlotJuggler with a pre-framed layout |
rqt_console, or grep ~/.ros/log |
"Read the ERRORs from ~/.ros/log and tell me what went wrong" — tracebacks included, no bag needed |
| Echo two topics in two terminals, correlate in a spreadsheet | "What's the correlation between current and voltage?" — topics live in one SQL relation, so joins and corr() are one question |
ros2 bag record -a and babysit the disk |
A standing edge pipeline: record continuously, keep only event windows, drop the rest |
| A bash loop over 200 bags | "Run this pipeline on every bag in the folder" — one pipeline, whole fleet, with a combined report |
scp/aws s3 sync scripts to ship data off the robot |
Upload to S3, GCS, or Azure as a pipeline step, checksum-skipping files already there |
| A different viewer per format: FlightPlot for PX4, MAVExplorer for ArduPilot, Blackbox Explorer for Betaflight | The same conversation for all of them — and ROS, MCAP, MQTT, Postgres, InfluxDB |
| Write a one-off pandas script per question | Ask the question; Bagel writes and runs the query |
One sentence of plain language, one answer — instead of a pipeline of commands and a script you'll delete tomorrow. Here's a sentence becoming a data pipeline that reduces a bag around detected events:
You can ask Bagel almost anything. For example:
What’s the correlation between current and voltage in the
/spot/status/battery_statestopic?
I think the robot hit a pothole. Can you check for sudden deceleration on the z-axis to confirm?
Can you help me tune the PID of my drone?
Time to put Bagel to the test: can it catch a drone doing barrel rolls? Spoiler: 🎉 It totally can.
When you ask a question, Bagel analyzes your data source’s metadata and topics to build a high-level understanding.
Based on your prompt, if further inspection is needed, Bagel identifies the most relevant topics and interprets their meaning and structure. Bagel then writes the relevant topic messages to an Apache Arrow file and uses DuckDB to generate and execute queries against it.
This process is repeated as needed, running new queries until Bagel finds the best answer to your question.
LLMs excel at language but struggle with math. Bagel overcomes this by generating deterministic DuckDB SQL queries. These queries are displayed for you to audit, and you can guide Bagel to correct any errors.
Three commands. That’s it.
[!TIP] Already have Claude Code? Just paste the link to this repo and tell Claude what environment you want:
Set up https://github.com/Extelligence-ai/bagel for ROS2 Kilted.
Claude will clone the repo, start Docker, and wire up the MCP connection for you.
Install Docker Desktop and Claude Code (or another MCP-enabled LLM).
git clone https://github.com/Extelligence-ai/bagel.git && cd bagel
docker compose run --service-ports ros2-kilted
Pick the service that matches your environment:
| Service | Use case |
|---|---|
ros2-kilted |
ROS2 Kilted (latest) |
ros2-jazzy |
ROS2 Jazzy |
ros2-iron |
ROS2 Iron |
ros2-humble |
ROS2 Humble |
ros1-noetic |
ROS1 Noetic |
ros1-noetic-cv |
ROS1 Noetic + CV |
px4 |
PX4 flight logs |
ardupilot |
ArduPilot flight logs |
betaflight |
Betaflight flight logs |
iot |
IoT / MQTT (live) |
[!TIP] To give Bagel access to your local files, edit
compose.yamlbefore starting Docker: uncomment and update thevolumessection under your chosen service.
Wait for this output:
INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
In a new terminal:
claude mcp add --transport sse bagel http://0.0.0.0:8000/sse
claude
Summarize the metadata of the ROS2 bag "./data/sample/ros2/mcap".
That’s it — you’re chatting with your data.
Swap step 2 for a local model — your data and your LLM stay on the machine:
brew install ollama && ollama serve & # or ollama.com
ollama pull qwen3:8b
uvx ollmcp --mcp-server-url http://localhost:8000/sse --model qwen3:8b
Model picks, expectations, and troubleshooting: Local LLMs guide.
Bagel works with any MCP-enabled LLM. Setup runbooks for tested alternatives:
Can’t find your LLM? Open a ticket.
Bagel learns new capabilities through POML files—a structured set of instructions that describe a “trick,” such as computing latency statistics.
For example, let’s define ./src/agent/examples/woof.poml.
<poml>
<task>
Count the topics in the data source.
If the count is odd, say "woof", else say "meow".
</task>
<output-format>
Return the sound, the topic count, and a few cute em