measure-mcp

measure-mcp

An AI tape measure. An MCP server that turns iPhone LiDAR captures, depth maps, room scans and photos with a reference card into real-world measurements that Claude (or any MCP client) can use.

• Kitchen: "How much does this bowl hold?" Measure your own bowls and pans, then scale a recipe to fit them.
• Rooms: "Will this 84 inch sofa fit through the door and on that wall?" Works from RoomPlan, Polycam, Scaniverse and other LiDAR scan exports.
• Amazon FBA: size tier, dimensional weight, fee estimate, how close you are to the next tier, and what to shrink to drop one.
• Moving and storage: cubic feet and weight from an inventory or a room scan, then the truck, container or storage unit that fits.

Every number comes back with a ± and a confidence level, and the server says plainly when it is guessing.

How it works

The AI never touches a sensor. Capture happens on the phone (the companion iOS app, a LiDAR scanner app, or the camera), and this server does the geometry on the files. It picks the best depth source available:

• Metric depth already in the file: a 3D scan from a free LiDAR scanner app such as Scaniverse (OBJ or PLY, zipped is fine), an iPhone Pro capture bundle, a depth TIFF/PNG/NPZ, or a RoomPlan USDZ/JSON export.
• A depth model on a plain photo (optional [depth] extra, Depth Anything V2 Small, Apache-2.0).
• A reference object in frame (credit card, US letter or A4 sheet, quarter) to fix the scale.

A plain photo with none of these has no real scale. The tools say so instead of inventing a number.

Install

pip install measure-mcp            # core: all tools, no ML
pip install "measure-mcp[usd]"     # + reading RoomPlan .usdz files
pip install "measure-mcp[depth]"   # + depth model for plain photos (pulls in PyTorch)

Python 3.10 or newer. uvx measure-mcp works too.

Claude Desktop

Add this to claude_desktop_config.json (Settings, Developer, Edit Config):

{
  "mcpServers": {
    "measure": {
      "command": "uvx",
      "args": ["--from", "measure-mcp[usd]", "measure-mcp"]
    }
  }
}

Claude Code

claude mcp add measure -- uvx --from "measure-mcp[usd]" measure-mcp

Load only what you need

All 34 tool descriptions take about 19k tokens of context. Set MEASURE_MCP_TOOLS to the groups you want (the core measure_* tools always load):

MEASURE_MCP_TOOLS	Tools	Context
(unset)	34	~19k tokens
kitchen	9	~4k tokens
room	14	~6k tokens
moving	15	~6k tokens
fba	14	~8k tokens

"measure": {
  "command": "uvx",
  "args": ["--from", "measure-mcp", "measure-mcp"],
  "env": { "MEASURE_MCP_TOOLS": "fba" }
}

Combine groups with commas, for example kitchen,room.

Getting files to the server

Tools take file paths on the machine running the server. When you share a photo in chat, save it to ~/measure-mcp-inbox (or AirDrop it to Downloads) and the AI finds it with measure_find_files. Files the tools write (floor plans, inventories) go to ~/measure-mcp-output, or wherever MEASURE_MCP_OUTPUT_DIR points. Tools never write anywhere else.

Try it

Measure the bowl in ~/measure-mcp-inbox/bowl_capture.zip and scale my brownie recipe to it: 1 cup butter, 2 cups sugar, 4 eggs, 1 1/4 cups flour. The original recipe is for a 9x13 pan.

Here's my living room scan (~/Downloads/living.usdz). Will an 84 x 37 x 34 in sofa get through the door, and does it fit on the wall with the window?

What FBA size tier is a 12 x 9 x 3 in box at 1.4 lb priced at $24, and how close is it to the next tier?

I'm moving out of a 2 bedroom: queen bed, 3-seat sofa, dining table with 4 chairs, 2 dressers, 30 medium boxes. What truck and storage unit do I need?

Tools (34)

Group	Tools
Core	measure_find_files, measure_depth_info, measure_open_capture, measure_object, measure_distance_between_points, measure_list_reference_objects
Kitchen	kitchen_measure_vessel, kitchen_vessel_from_dimensions, kitchen_scale_recipe
Room	room_load_scan, room_summary, room_list_walls, room_list_openings, room_list_objects, room_check_fit, room_check_doorway, room_floor_plan_svg
Amazon FBA	fba_analyze, fba_size_tier, fba_fee_estimate, fba_tier_headroom, fba_audit_listing, fba_draft_remeasure_request, fba_measure_package, fba_data_status
Moving	moving_estimate_from_inventory, moving_estimate_from_scan, moving_estimate_object, moving_cube_sheet_lookup, moving_recommend_truck, moving_recommend_container, moving_recommend_storage, moving_export_inventory, moving_data_info

Conventions shared by every tool: the input file is path, sizes are dims ("12 x 9 x 3 in", a list, or a dict), weights are weight (1.4 or "1.4 lb"), output units are units (metric, imperial, both), and errors come back as {"error": {"code", "message", "hint"}}. An argument a tool doesn't take is rejected with the closest valid name, never silently ignored.

Accuracy

Measured on synthetic scenes with known answers and simulated iPhone LiDAR noise (256 x 192 depth, σ 5 mm). Real-world accuracy will be somewhat worse. Treat these as a ceiling, not a promise.

What	Result
Bowls, pans and pots (12 vessels, 3 camera angles, 108 renders)	median capacity error 0.9%, 90th percentile 2.5%; the reported ± contained the truth in every render
Rim diameter	worst error 0.35 cm
Bowls from scanner-app meshes (OBJ/PLY, cm or m, Y-up or Z-up, holes, clutter)	median capacity error 0.7% on 16 random scans; the ± contained the truth every time
Boxes (single view, any rotation)	footprint within 0.5 cm across 240 camera views
Room walls from a LiDAR point cloud	mean 0.13 cm, worst 0.38 cm without drift; 3.5 cm worst with 2 cm drift
FBA tier risk flags	caught 100% of boxes whose tier was wrong because of measurement noise
Moving estimates (studio to 3 bedroom)	all within published industry ranges

Known limits:

• Steep camera angles hide the bottom of deep pots. At 30° the tools report low confidence and a wide ±.
• Shallow pans (1 inch deep) come back as low confidence.
• A guessed camera lens widens the error bars. For depth files without camera intrinsics the tools say "field of view unknown" and cap confidence at low.
• Portrait-mode photos carry relative depth only. iPhone Portrait mode depth is not a real-world distance, so it needs a reference object.
• Room accuracy figures are synthetic. Scanner drift on real phones is the biggest unknown, and real-scan validation is on the roadmap.

Data and estimates

• FBA: size tiers and fee tables live in a versioned data file with an as-of date and sources. Several 2026 figures come from secondary sources and are flagged unverified; fba_data_status lists every gap. These are estimates. Amazon's own measurement and fee schedule govern.
• Moving: the cube sheet, truck capacities, containers and storage units come from published sources, each row marked verified or not. There are no prices in the data.

Companion iOS app (in progress)

ios/ holds the source for a small capture app. One tap saves an iPhone Pro LiDAR depth map, photo and camera calibration as a zip bundle that measure_open_capture reads, and it exposes App Intents ("Measure object") to Siri and Shortcuts. It has not been compiled or run on a device yet. See ios/README.md.

HTTP mode

measure-mcp --transport streamable-http                          # 127.0.0.1:8765, local only
MEASURE_MCP_TOKEN=... measure-mcp --transport streamable-http --host 0.0.0.0

The server reads files on its host, so any non-loopback address requires a bearer token.

Development

git clone https://github.com/purahmanian/measure-mcp && cd measure-mcp
python -m venv .venv && . .venv/bin/activate
pip install -e ".[dev,usd]"
python -m pytest -q          # about 1,300 tests, no network
python -m ruff check .
PYTHONPATH=src:. python bench/kitchen_bench.py --quick

Design notes for each feature are in designs/.

License

MIT, Puya Ventures LLC. Depth Anything V2 Small (optional extra) is Apache-2.0.

Built by

Puya Rahmanian. Need an MCP server or an AI integration built for your product? puyarahmanian.com