Maskbolt Segment Anything on CPU

Official registry AI and agents

Segment Anything on CPU for agents: cut out objects, find every object, export masks and outlines.

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

MCPB package https://github.com/skulitom/Maskbolt/releases/download/v0.1.0/maskbolt-windows-x64.mcpb

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. Maskbolt
  2. Install
  3. CLI
  4. MCP
  5. Speed (Ryzen 9 7950X3D, 16 cores, 64 GB, CPU only, release build, 2026-10-23)
  6. Limits
  7. Licence

Maskbolt

Segment Anything (SAM) on your CPU, for AI agents and scripts. Maskbolt is one Rust binary: an MCP server that lets an agent open an image, find every object or click one, look at numbered previews, fix mistakes, and export cutouts, outlines and masks; and a CLI for the same jobs in one command. No Python, no GPU.

  • One click cuts an object out as a transparent PNG; the three nested SAM masks (part, object, group) are one argument away.
  • "Find everything" runs grid prompts over the whole image or over overlapping tiles (each re-encoded at full model resolution, so small objects show up), drops masks cut by a tile edge, background patches, duplicates and masks inside others, and says why.
  • Every reply that changes masks says how many there are, why candidates were dropped, and which patches still look uncovered ("find more like these"), so an agent knows what is left.
  • Exports: overlay PNG (numbered masks, zoomable), cutout PNG, SVG (outer loops and holes, even-odd, optional millimetres), JSON (outlines with holes, area, box, score), labels PNG (one colour per mask).
  • Masks are stored cropped to their boxes, so a dense image with thousands of candidates stays small.

Install

Download the archive for your computer from the latest release: maskbolt-v0.1.0-x86_64-pc-windows-msvc.zip, maskbolt-v0.1.0-x86_64-unknown-linux-gnu.tar.gz (glibc 2.35 or newer), maskbolt-v0.1.0-aarch64-apple-darwin.tar.gz or maskbolt-v0.1.0-x86_64-apple-darwin.tar.gz. Check it against the release's SHA256SUMS, extract it and put maskbolt (maskbolt.exe on Windows) on your PATH; maskbolt version prints maskbolt 0.1.0. Each archive also holds the licence, THIRD_PARTY.txt and THIRD_PARTY_NOTICES.md (the licence texts of the Rust dependencies in that build). Or build it with Rust 1.89 or newer:

cargo install --git https://github.com/skulitom/Maskbolt --tag v0.1.0 --locked

Maskbolt is not on crates.io.

The first run downloads the model into a user cache folder and checks its SHA-256:

ModelFlagDownloadLicence
SAM ViT-B (Meta)default, --model vit-b375 MBApache-2.0
MobileSAM (TinyViT)--model tiny41 MBApache-2.0

Cache folder: %LOCALAPPDATA%\maskbolt\models on Windows, $XDG_CACHE_HOME/maskbolt/models or ~/.cache/maskbolt/models elsewhere; set MASKBOLT_MODEL_DIR to use another. maskbolt fetch downloads now. Maskbolt keeps the checked .pth next to the converted safetensors and records the converted file's SHA-256 in <name>.safetensors.sha256; every load re-checks it and rebuilds the file from the .pth if it does not match. Several Maskbolt processes can share the folder (unique temp names, atomic renames). Disk use per model is the checkpoint plus the converted file (ViT-B about 750 MB, MobileSAM about 81 MB). Minimum Rust: 1.89.

CLI

maskbolt cutout examples/chelsea.png --point 200,150 --out cat.png maskbolt cutout examples/coffee.png --point 300,225 --level largest --fill-holes --out cup-set.png maskbolt cutout photo.jpg --point 410,220 --exclude 380,300 --box 300,120,520,340 --out thing.png maskbolt find examples/corn.png --out kernels.json --overlay kernels.png maskbolt find examples/corn.png --tiles 400 --fill-gaps --out kernels.json --overlay kernels.png maskbolt find examples/bed.png --grid 12 --min-area 0.01 --max-area 0.3 --drop-hole-views \ --out offcuts.json --svg offcuts.svg --mm-per-px 0.5 maskbolt mcp --root C:\work\images maskbolt version

Outputs that already exist are refused unless you pass --force; the input image is never overwritten. One find uses at most 64 tiles and a 48x48 grid; past that it says how many tiles your settings give, about how long they would take, and which tile size fits. A tiled find has no whole-image pass: objects larger than about a third of a tile are dropped, so run a plain find first for big objects. Images are recognised by their content, not their extension, EXIF orientation is applied, and the limit is 100 megapixels.

--level: best (default, highest predicted IoU), smallest (a part), middle (the object), largest (the object with what it holds). find prints a JSON report (counts, drop reasons, timings, what is still uncovered).

MCP

{"mcpServers": {"maskbolt": {"command": "maskbolt", "args": ["mcp", "--root", "C:\\work\\images"]}}}

  • Claude Code: the plugin registers this server, rooted at the folder Claude Code runs in, and a maskbolt skill with the loop below. It needs maskbolt on PATH (see Install):

    claude plugin marketplace add skulitom/Maskbolt claude plugin install maskbolt@maskbolt

    or, inside Claude Code 2.1.275 or later, in one step: /plugin install maskbolt --marketplace skulitom/Maskbolt.

  • Claude Desktop (Windows x64): download maskbolt-windows-x64.mcpb from the release, double-click it (or Settings > Extensions > Advanced settings > Install Extension) and choose your image folder.
  • MCP Registry: listed as io.github.skulitom/maskbolt, with the Windows bundle and its SHA-256.

Long tiled finds take minutes: the plugin gives Maskbolt's tools a 10-minute timeout; other clients may stop waiting sooner (see Limits).

Tools: maskbolt_open, maskbolt_find, maskbolt_click, maskbolt_view, maskbolt_remove, maskbolt_gaps (add: true clicks the candidates), maskbolt_fill_holes, maskbolt_export. Find and view replies include a numbered PNG; click replies include a zoom on the new mask. The server reads and writes only inside --root (default: the folder it starts in; it refuses to start when that is a drive or file-system root or your home folder). Paths are checked before any file access: UNC and device paths (\\server\share, \\?\, \\.\, //host), other drives and anything .. takes outside the root are refused with one message. Exports never replace an existing file unless the call passes overwrite: true, and never replace the open image. View replies give image_x = region.x + preview_x / scale; previews are at most 1568 px on the long side. On first use the model downloads in the background; maskbolt_open says its size and asks to be called again if it is not done within 25 s.

The loop that works: open; find (add tile: 400 for small or dense objects) or click; view (zoom with region); remove wrong masks; click missed ones; gaps with add: true when replies list uncovered patches; export.

Speed (Ryzen 9 7950X3D, 16 cores, 64 GB, CPU only, release build, 2026-10-23)

StepSAM ViT-BMobileSAM (--model tiny)
Load model0.2 s0.05-0.2 s
Encode one image (any size; SAM works at 1024 px)6.3-6.8 s4.2-4.3 s
One click (decode + upsample the three masks)0.11-0.12 s0.13-0.14 s
cutout on chelsea.png, whole command6.8 s, 2.0 GB peak5.1 s, 0.5 GB peak
find corn.png 1200x1198, 32x32 grid30.8 s wall (23.5 s after encode), 228 masks, 3.3 GB peak29.4 s wall, 234 masks, 3.0 GB peak
find --tiles 400 (25 tiles, 16x16 each)221 s wall, 247 masks, 7.0 GB peaknot measured
find --tiles 400 --fill-gaps246 s wall, 256 masksnot measured

Tiles encode 4 at a time (MASKBOLT_ENCODE_THREADS, default 4; each holds about 1.2 GB at its peak; 1 gives about 3.3 GB peak and 332 s for the tiled corn run). Prompts decode on up to 16 threads (MASKBOLT_DECODE_THREADS). For comparison, the Node prototype (8-bit ONNX) took 199 s for the 32x32 corn run and 416 s for the tiled one.

Limits

  • CPU only. Encoding is the slow step (about 6.5 s per image or tile with ViT-B); MobileSAM encodes in about 4.2 s here, only 1.5x faster, because candle's CPU convolutions are slow. Memory peaks at 2-3.3 GB for one image and about 7 GB for tiled finds with 4 parallel encodes.
  • Long tiled finds can outlast an MCP client's tool timeout (the 400 px corn run, 25 tiles, takes 3.5 minutes; the cap of 64 tiles is about 4 to 10 minutes). There are no progress notifications yet.
  • SAM's masks miss thin parts and leave holes; use level, extra include/exclude points, and fill_holes.
  • Outlines are simplified with Douglas-Peucker (1.2 px by default; simplify_px in MCP exports); edges move by up to that tolerance. Coordinates are image pixels from the top-left corner (pixel x covers [x, x+1]).
  • The gap check is a colour heuristic: it finds look-alikes of what is already masked, not new kinds of object.

Licence

MIT. Model weights: Apache-2.0 (Meta's SAM; MobileSAM). src/sam_decoder.rs is adapted from candle (MIT OR Apache-2.0); candle's licence texts are in THIRD_PARTY.txt. Release archives and the MCPB bundle add THIRD_PARTY_NOTICES.md with the licence texts of every Rust dependency in that build. Releasing: see mcpb/PUBLISHING.md.

Frequently asked questions

What is Maskbolt Segment Anything on CPU?

Segment Anything on CPU for agents: cut out objects, find every object, export masks and outlines.

How do I install Maskbolt Segment Anything on CPU?

Follow the installation steps in its documentation, shown on this page and in its repository.

Does Maskbolt Segment Anything on CPU need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is Maskbolt Segment Anything on CPU open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains Maskbolt Segment Anything on CPU?

It is published by skulitom. AI.tn lists it but does not maintain or endorse it.

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