mcpbeat Sign in

CutOptim Engine MCP Server

answering

CutOptim Engine is answering right now. Last checked 14 min ago. 179 installs a week from npm. It exposes 10 tools. Last commit 24 Oct 2026.

Cutting optimization: 2D panels, 1D bars, timber and true-shape nesting via the CutOptim API.

Installs per day peak 179 · avg 5
a month agotoday
Uptime history 69 hours of history
69 hours agonow
100.0%
Uptime 24h
91 of 91 checks
10
Tools
read from the server
124 ms
Response time
average over 24h
179
Installs / week
npm and PyPI

What changed 8

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 24 October 2026. No other catalogue keeps this.

25 Oct 2 tools changed the parameters they ask for optimize_2d, validate_2d
24 Oct 4 tools changed the parameters they ask for optimize_1d, optimize_wood, validate_1d and 1 more
24 Oct a tool changed version2 times that day
and 1 more, back to 24 October 2026

Nothing serious here today

Today is the operative word: we check CutOptim Engine every 15 minutes and re-read its code on every release. Watch it and you find out the day that stops being true.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 14 min ago.

run in your terminal
claude mcp add engine --transport http https://api.cutoptim.com/mcp
Settings → Connectors → Add custom connector, then paste this address
https://api.cutoptim.com/mcp
~/.codex/config.toml
[mcp_servers.engine]
url = "https://api.cutoptim.com/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "engine": {
      "url": "https://api.cutoptim.com/mcp"
    }
  }
}
.vscode/mcp.json
{
  "servers": {
    "engine": {
      "type": "http",
      "url": "https://api.cutoptim.com/mcp"
    }
  }
}

Available tools 10

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

optimize
optimize_1d
Metered: one Engine API call. Cut lengths from bars, tubes, profiles or rolls and get the cutting pattern per bar. 1D / linear optimization (bars, profiles, tube). Cut parts of a given length from stock bars. Returns the rods used, the position of every part along each rod, the leftover length and the yield metrics.
optimize_2d
Metered: one Engine API call. Cut rectangular parts from sheets (panels, plywood, glass, sheet metal) and get the layout, cut plan and yield. 2D panel optimization. Nest rectangular parts into rectangular sheets. Returns the placement of every part, the remaining offcuts, the guillotine cut plan and the yield metrics. Parts named in `matchedSets` (grain matching) are cut side by side from one piece of board, one kerf apart; each placed member says which set and position it is.
optimize_nest
Metered: one Engine API call (needs an API key; not available with the demo key). Nest free-form polygon parts on sheets. True-shape (irregular / polygon) nesting. Pack ARBITRARY POLYGONS onto fixed sheets, letting parts interlock into each other's concave pockets — an L-bracket, a gasket, a laser-cut profile. This is a different class from the rectangular modes: a part is an outline (`polygon`, optional interior `holes`), not a width×height, so a bounding-box packer's notch-air is reclaimed. On a representative 272-part job it uses 6 sheets where the same parts by bounding box need 9 (−33 % material). Two things the rectangular API cannot express come for free here: per-sheet EXCLUSION ZONES (`stock[].exclusions` — a defect, a clamp footprint, a pre-printed area; a `quality`-0 zone is a no-go region for any part), and true-shape holes. Material partition (`material`) and the opaque `meta` passthrough work exactly as on the other modes. ⚠️ `density` is the placed POLYGON area over used sheet area — the honest fill, with concave pockets counted as empty. It is NOT comparable to a rectangular packer's `yieldPct` (which counts each bounding box as solid and so reads higher for a worse result); the cross-comparable metric is `sheetCount` on the same parts. A placed part carries the rigid transform (`rotation` in degrees, then `x`/`y` translation), NOT a re-emitted polygon: your input outline plus that transform reconstructs the placement exactly. Deterministic (a pinned seed); set `options.seed` to reproduce a layout. Engines: `lbf` (default) is synchronous and instant. Besides its own pass it tries the parts restricted to the right angles they allow, and their bounding boxes packed by an exact rectangle packer — which finds grids a sampling search misses (three 500-wide parts on a 1500-wide sheet at zero spacing) — and keeps whichever ranks best; on a tie, the plan that fills its sheets front to back, then the one with fewer parts off the right angles. `max` is ASYNCHRONOUS: the call answers **202** with a `jobId`, and you poll `GET /v1/jobs/{id}` for the result — the same response shape, with `engine: "max"`. It runs the `lbf` pass first and then a time-budgeted search (`options.timeBudgetMs`, default 60 s, 10–180 s) that fills sheets one at a time looking for a layout with FEWER sheets; it never returns more sheets than `lbf` would — nor a plan that ranks worse by the sheet-size ranking below — and says in `warnings` when it kept the `lbf` layout. Several sheet sizes per material and `respectStock` are modelled; a `polygon` sheet or `exclusions` (which its strip search cannot model) are served by the `lbf` pass inside the job, with a warning — never refused. `sparrow` is a deprecated alias of `max`. **Sheet sizes and cost.** With two or more stock rows for a material, both engines compare arrangements (your order, each row alone, cheapest-first) and then re-nest each sheet — the least-filled first — onto the cheaper or smaller rows, so a plan may MIX sizes (e.g. two large sheets and a small last one). Ranking: most must-cut (`priority`) parts placed → most parts placed → (with `minimizeCost`) lowest total price → least total sheet area → fewest sheets. `sheets[].stock` and `stockUsage[]` say which row each sheet came from. **Used area.** `sheets[].usedWidth` / `usedHeight` are the farthest extent of any placed part from the sheet origin (the corner the parts are packed into), `usedArea` their product, and `stockUsage[].usedArea` the sum per row — the numbers an ERP needs to charge a partial sheet (full sheet, a horizontal or vertical cut, or the bounding box; the charging rule stays yours). Both engines compact the least-filled sheet of each material: its parts are re-nested on the same sheet with rotation SUBSETS of what each part allows ({0,180}, {0,90,180,270}, a single orientation — never a new angle) and the variant with the smallest used area is kept; with `max` that sheet is then also re-packed by the search itself, along the sheet's height and (on a one-sheet plan) along its width, and the tighter layout is kept under the same rule. Sheet count, sizes, price and placed parts never change. `options.compact: false` returns the uncompacted layout, and `options.compactFor` (`box` · `horizontal` · `vertical`) picks the measure to minimise when you charge a full-width or full-height strip. `metrics.cutLength` / `sheets[].cutLength` is the summed perimeter of the placed parts' outlines and holes; `pierces` counts one per closed contour (no common-line cutting, lead-ins or micro-joints — your CAM adds those). **Parts from CAD files.** A part may carry `source` — an SVG or DXF document — instead of `polygon` coordinates; the server reads the outline (and its holes) out of the file with the same parser the CutOptim app uses. The file replaces ONLY the geometry, and the response then carries an `imported` block saying what the files contributed. One file describes ONE part; `POST /v1/import/nest` splits a multi-part drawing first. Nothing is stored: the bytes are parsed in memory and gone with the response. Because a drawing is far larger than a JSON parts list, this path accepts a **10 MB body** (the rectangular endpoints keep 1 MB), with per-file and per-request character caps on `source.content`.
optimize_wood
Metered: one Engine API call. Cut timber parts grouped by cross-section from matching stock lengths. Timber optimization (cross-section-aware 1D). Cut lengths from bars that have a CROSS-SECTION. Unlike `/v1/optimize/1d`, where every bar can serve every part, a 50×150 part can only come out of 50×150 stock — so the job is split by cross-section, each section is matched to its own stock and solved separately, and the response carries both per-section and job-wide totals. Fanning the same job out into several `/v1/optimize/1d` calls is not equivalent: it costs one call per section against your quota, moves the stock matching to your code, and cannot distinguish "no material of this section" from "did not fit" — which this endpoint reports separately as `unmatched` and `unplaced`. `sw` and `sh` may be given in either order: 50×100 and 100×50 are the same bar turned over and are matched as one section. The response reports the normalised short/long form.
validate
validate_1d
Free, no key needed: checks a 1D request without solving it. Same arguments as optimize_1d (field descriptions there). Validate a 1D request — free, no key, no quota, no solve. Schema-validate the SAME body `/v1/optimize/1d` takes, WITHOUT solving or spending a call. See `/v1/validate/2d`.
validate_2d
Free, no key needed: checks a 2D request without solving it. Same arguments as optimize_2d (field descriptions there). Validate a 2D request — free, no key, no quota, no solve. Schema-validate the SAME body `/v1/optimize/2d` takes, WITHOUT solving or spending a call. 200 `{valid:true, warnings}` (warnings flag parts that fit no stock — an approximate, solve-free probe), or 400 with the exact bad field path. Open like `/v1/health`: validate payloads during integration before you even have a key. The engine verdict matches the optimize path exactly, `engine: "max"` included: a body this endpoint accepts is one `/v1/optimize/2d` will accept, and one it refuses (400 for an option the max tier cannot model, 503 where that tier is not enabled) is refused there too.
validate_nest
Free, no key needed: checks a nesting request without solving it. Same arguments as optimize_nest (field descriptions there). Validate a nest request — free, no key, no quota, no solve. Schema-validate the SAME body `/v1/optimize/nest` takes, WITHOUT solving or spending a call. Reports `engineEnabled` (whether the nesting engine is built into this deployment) and warns about parts whose bounding box fits no stock sheet. For `engine: "max"` the verdict matches the optimize path exactly: 400 for anything outside the preview scope, 503 where the async tier is not enabled, and otherwise a warning that the job will be asynchronous. See `/v1/validate/2d`.
validate_wood
Free, no key needed: checks a timber request without solving it. Same arguments as optimize_wood (field descriptions there). Validate a wood request — free, no key, no quota, no solve. Schema-validate the SAME body `/v1/optimize/wood` takes, WITHOUT solving or spending a call. Warnings flag parts with no matching-section stock long enough. See `/v1/validate/2d`.
job
get_job
Free (needs the key that submitted the job). Reads an engine "max" job; when it succeeded, the plan is in result. Poll an async max-engine job. Fetch the status (and, when finished, the plan) of a job submitted with `engine: "max"`. A job is `queued`, then `running`, then `succeeded` (with `result` = the same response shape a synchronous solve of that `mode` returns, carrying `engine: "max"` — a 2D plan for `mode: "2d"`, a nest for `mode: "nest"`) or `failed` (with `error`). Polling spends no quota, but the key must still be usable: a revoked key or a lapsed API subscription is refused with 403 here exactly as it is on every other authenticated path. A caller sees only its own jobs — a job id belonging to another account is reported as 401 "job not found", indistinguishable from one that does not exist, so ids cannot be probed.
usage
get_usage
Free (needs an API key). Shows the plan, the calls used and left this month, and when the period ends. The account's current-month usage and quota. Read-only. Does not consume quota and does not send rate-limit headers. The period is the UTC calendar month.

Endpoints

URLTransportStateLatencyChecked
https://api.cutoptim.com/mcp streamable-http answering 107 ms 14 min ago

Alternatives to CutOptim Engine

same job, measured the same way
The Gem Smith
by evozim

Premium SEO and Answer Engine Optimization (AEO) oracle for AI agents.

answering
SEO-Ghost
by evozim

AEO intent and search presence optimization engine.

1 tools answering
SmartCut
by smartcut

Cutlist optimization for sheet, linear and roll stock, with grain, kerf, nesting and saw exports.

answering
SAGE — Solver-Augmented Grounding Engine
by pragnakar

LLM-native optimization solver: LP, MIP, portfolio & scheduling via HiGHS/OR-Tools.

142 installs/wk local only
Twig
by kind-ling

MCP server quality scoring and optimization for the agent economy.

31 installs/wk local only
Pedra
by pedra-ai

AI real-estate photo editing and 360° virtual tours via the Pedra API: staging, renovation, video.

248 installs/wk local only
E
Engineer Your Data
by eghuzefa

MCP server for data engineering: validation, transformation, visualization, and APIs.

110 installs/wk local only
Enrow MCP
by enrow

Find and verify professional emails and phone numbers via the Enrow API.

answering

CutOptim Engine — questions

Answers built from our own checks of this server.

What can CutOptim Engine do?
It exposes 10 tools, read directly from the server on our last check. Among them: get_job, get_usage, optimize_1d, optimize_2d, optimize_nest, optimize_wood and 4 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
What is CutOptim Engine mostly used for?
Its tools cluster around optimize and validate. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.
Is CutOptim Engine working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 124 ms. The bar chart above shows every period we have measured.
How do I connect CutOptim Engine?
Copy the ready config from this page. We generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each in the form that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does CutOptim Engine need an API key?
No. CutOptim Engine completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 10 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is CutOptim Engine?
It answers our handshake in 124 ms on average, which is faster than 93% of all working MCP servers we measure. That puts it in the quick quarter of the ecosystem. The comparison comes from our own checks across the whole registry, every 15 minutes.
How many people use CutOptim Engine?
The npm package @cutoptim/mcp was installed 179 times in the last week. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.
Is CutOptim Engine open source?
The code is open source: it is published under the MIT licence, written in TypeScript, 0 stars on GitHub and 1 open issue. The licence covers the code in the repository, not the hosted service at api.cutoptim.com, which runs on its operator's own terms.