measure-mcp

io.github.purahmanianPublished by GitHub account purahmanianv0.1.0Updated Oct 25, 2026

AI tape measure: cookware capacity, recipe scaling, room fit, Amazon FBA size tiers, moving volume.

The publisher's own server: Measure

VerifiedSTDIODesktop onlyFiles & StorageData & Analytics

Overview

AI-generated overview

An AI tape measure that turns iPhone LiDAR scans, depth files and reference-card photos into real-world measurements for cookware, rooms, FBA boxes and moving.

What you can do
Find and open capture files, inspect depth sources and list reference objects for scale calibration, and measure objects or distances between points with a ± and confidence level.
Requirements
Python 3.10 or newer, installed locally via pip or uvx. Optional extras add RoomPlan USDZ reading (usd) and a depth model for plain photos (depth, pulls in PyTorch). Tools read file paths on the machine running the server; output goes to a fixed output directory unless MEASURE_MCP_OUTPUT_DIR is set. MEASURE_MCP_TOOLS limits which tool groups load. HTTP mode on a non-loopback address requires MEASURE_MCP_TOKEN.
Before you install
Tools read files from paths on the host and write floor plans and inventories to an output directory. FBA fee and size-tier figures are estimates, some flagged unverified, and Amazon's own measurement and fee schedule govern. Accuracy figures come from synthetic scenes, so real-world results will be worse. A plain photo without depth data or a reference object has no real scale.

Installation

In SourceWeft

  1. Open measure-mcp in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

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:

  1. 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.
  2. A depth model on a plain photo (optional [depth] extra, Depth Anything V2 Small, Apache-2.0).
  3. 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

bash
pip install measure-mcp            # core: all tools, no MLpip install "measure-mcp[usd]"     # + reading RoomPlan .usdz filespip 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):

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

Claude Code

bash
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_TOOLSToolsContext
(unset)34~19k tokens
kitchen9~4k tokens
room14~6k tokens
moving15~6k tokens
fba14~8k tokens
json
"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)

GroupTools
Coremeasure_find_files, measure_depth_info, measure_open_capture, measure_object, measure_distance_between_points, measure_list_reference_objects
Kitchenkitchen_measure_vessel, kitchen_vessel_from_dimensions, kitchen_scale_recipe
Roomroom_load_scan, room_summary, room_list_walls, room_list_openings, room_list_objects, room_check_fit, room_check_doorway, room_floor_plan_svg
Amazon FBAfba_analyze, fba_size_tier, fba_fee_estimate, fba_tier_headroom, fba_audit_listing, fba_draft_remeasure_request, fba_measure_package, fba_data_status
Movingmoving_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.

WhatResult
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 diameterworst 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 cloudmean 0.13 cm, worst 0.38 cm without drift; 3.5 cm worst with 2 cm drift
FBA tier risk flagscaught 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

bash
measure-mcp --transport streamable-http                          # 127.0.0.1:8765, local onlyMEASURE_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

bash
git clone https://github.com/purahmanian/measure-mcp && cd measure-mcppython -m venv .venv && . .venv/bin/activatepip install -e ".[dev,usd]"python -m pytest -q          # about 1,300 tests, no networkpython -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

Source: README.md at commit b537255

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.1.0LatestOct 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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