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Moltline Optimize MCP Server

answering

Moltline Optimize is answering right now. Last checked 14 min ago. It exposes 11 tools. Last commit 13 Sep 2026.

Vehicle routing, 3-D packing, cutting stock, rostering and knapsack with OR-Tools. 7 of 11 free.

Uptime history 12 days of history
12 days agonow
100.0%
Uptime 24h
91 of 91 checks
11
Tools
read from the server
336 ms
Response time
average over 24h
0
Stars
last commit 13 Sep 2026

Nothing serious here today

Today is the operative word: we check Moltline Optimize 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 optimize --transport http https://mcp.moltlinestudio.com/optimize
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "optimize": {
      "url": "https://mcp.moltlinestudio.com/optimize"
    }
  }
}
~/.codex/config.toml
[mcp_servers.optimize]
url = "https://mcp.moltlinestudio.com/optimize"
.cursor/mcp.json
{
  "mcpServers": {
    "optimize": {
      "url": "https://mcp.moltlinestudio.com/optimize"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "optimize": {
      "url": "https://mcp.moltlinestudio.com/optimize"
    }
  }
}

Available tools 11

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

cutting
cutting_stock_1d
Least-waste cut plan for bars, pipes or boards from stock lengths, with saw kerf. FREE. Typical input {"stock": [{"length": 6000, "cost": 30}], "parts": [{"length": 2200, "qty": 3}, {"length": 1500, "qty": 4}], "kerf": 3} returns {"bars": [{"stock_length": 6000, "cuts": [2200, 2200, 1500], "waste": 94}], "bars_used": 3, "waste_pct": 4.2, "solver_status": "OPTIMAL"}. Minimises total stock cost (or count when no cost); CP-SAT proves optimality when it finishes inside the time limit and otherwise returns the best plan found as FEASIBLE. Use for a cut list of up to 200 pieces. Not for sheets: use cutting_stock_2d. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "stock and parts must be non-empty lists (<value> and <value>)"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
cutting_stock_2d
Guillotine cut layouts for rectangular parts from sheets, with kerf and grain. PREMIUM (license). Typical input {"sheets": [{"id": "ply", "l": 2440, "w": 1220, "qty": 5}], "parts": [{"id": "side", "l": 800, "w": 400, "qty": 6}], "kerf": 3} returns {"sheets_used": 1, "layouts": [{"sheet": "ply", "placements": [{"id": "side", "x": 0, "y": 0, "l": 800, "w": 400, "rotated": false}], "fill_pct": 64.5, "offcuts": [...]}], "unplaced": []}. Every cut is a guillotine cut (edge to edge): the sheet is ripped into strips and each strip cross-cut, which is what a panel saw does; grain true forbids rotating parts unless a part sets rotate true. Use for cabinet, sign and sheet-metal cut lists. Not proven optimal: a best-fit shelf heuristic, reported as such. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "sheets and parts must be non-empty lists (<value> and <value>)"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
pack
pack_bins
Place up to 20 boxes into containers or pallets with rotation, weight and support rules. FREE. Typical input {"items": [{"id": "A", "l": 60, "w": 40, "h": 30, "weight": 12, "qty": 4}], "containers": [{"id": "pallet", "l": 120, "w": 80, "h": 150, "max_weight": 500, "qty": 2}]} returns {"containers_used": 1, "containers": [{"placements": [{"id": "A", "x": 0, "y": 0, "z": 0, "l": 60, "w": 40, "h": 30}, ...], "volume_fill_pct": 20.0}], "unplaced": []}. rotation per item: any, upright (rotate around the vertical axis only) or fixed; fragile items carry nothing; rules.min_support (default 0.6) is the share of a box's base that must rest on the floor or on boxes below. Use to decide pallet or carton count before booking freight. Not proven optimal: it is a first-fit-decreasing heuristic, reported as such. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
pack_bins_large
Same packer as pack_bins for up to 300 item units and 200 containers. PREMIUM (license). Typical input {"items": [{"id": "SKU1", "l": 40, "w": 30, "h": 20, "weight": 5, "qty": 120}, ...], "containers": [{"id": "euro-pallet", "l": 120, "w": 80, "h": 180, "max_weight": 800, "qty": 10}]} returns the same shape as pack_bins: containers with placements, fill percentages, weights and any unplaced units. Use for order consolidation and load planning. Not proven optimal (first-fit decreasing on extreme points, reported as such). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
route
route_plan
Order up to 12 stops into the shortest single-vehicle route on your distance matrix. FREE. Typical input {"stops": [{"id": "depot"}, {"id": "A"}, {"id": "B"}], "matrix": [[0, 5, 9], [5, 0, 4], [9, 4, 0]]} returns {"routes": [{"vehicle": 0, "stops": [...], "distance": 18.0}], "total_distance": 18.0, "solver_status": "FEASIBLE", "note": "..."}. The matrix is in your units (km, minutes, cost) and must be square with the depot at index 0 unless depot says otherwise; optional demand per stop with vehicle_capacity turns it into a capacity check. Use for one driver's day or a courier's loop. Not for several vehicles or time windows: use route_plan_fleet. Not a map service: bring your own distances or call distance_matrix_haversine for straight-line values. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "stops must be a list of stop objects, depot first"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
route_plan_fleet
Capacitated, time-windowed routing for a fleet over up to 200 stops. PREMIUM (license). Typical input {"stops": [{"id": "depot", "window": [480, 1080]}, {"id": "A", "demand": 3, "window": [540, 720], "service_min": 10}, ...], "matrix": [[...]], "vehicles": [{"id": "van1", "capacity": 10}, {"id": "van2", "capacity": 8, "max_distance": 120}]} returns {"routes": [{"vehicle": "van1", "stops": [{"id": "A", "arrive_min": 545, ...}], "distance": 42.5, "load": 9}], "unserved": [], "solver_status": "FEASIBLE"}. Windows and service times are minutes from the start of the day; travel time comes from time_matrix (minutes) or, if absent, the distance matrix read as minutes. Set drop_penalty to allow stops to be left unserved at that cost instead of returning INFEASIBLE. Use for daily dispatch. Not a map service; bring your own matrices. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "stops must be a list of stop objects, depot first"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
distance
distance_matrix_haversine
Straight-line (great-circle) distance matrix from coordinates. FREE. Typical input {"points": [{"id": "depot", "lat": 51.5, "lon": -0.12}, {"id": "A", "lat": 51.52, "lon": -0.1}]} returns {"matrix": [[0, 2.6], [2.6, 0]], "unit": "km", "kind": "straight-line (haversine), not road distance"}. Use when you have no road matrix and a straight-line approximation is acceptable, or to sanity-check one. Not road routing: real driving distances are longer and the difference is not uniform. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "points must be a list of at least two <value> objects"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
explain
explain_solution
Plain-language summary of a solution from this server and the constraints that bind. FREE. Typical input {"solution": <result of route_plan_fleet>} returns {"summary": "2 vehicles serve 14 stops over 96.4 km; 1 stop unserved", "binding_constraints": ["van2 is at 100% of capacity", "stop C arrives at the end of its window"], "status": "FEASIBLE"}. It recognises results from route_plan, route_plan_fleet, pack_bins, cutting_stock_1d, cutting_stock_2d, roster_shifts and knapsack_select by their fields. Use to turn solver output into a message for a dispatcher or a shop floor. Not a re-solve. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "solution must be the result object returned by a solve tool on this server"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
knapsack
knapsack_select
Choose the items that maximise value under one or more capacity limits. FREE. Typical input {"items": [{"id": "a", "value": 60, "weight": 10, "cost": 120}, {"id": "b", "value": 100, "weight": 20, "cost": 300}], "limits": {"weight": 25, "cost": 400}} returns {"selected": ["a"], "value": 60, "used": {"weight": 10, "cost": 120}, "slack": {"weight": 15, "cost": 280}, "solver_status": "OPTIMAL"}. Any numeric item field named in limits is a constrained resource; qty lets an item be taken several times. Use for budgets, cargo, campaign or feature selection. Not for dependencies between items. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "items must be a non-empty list of <value>"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
roster
roster_shifts
Assign staff to shifts under availability, skills, hour caps and rest gaps. PREMIUM (license). Typical input {"staff": [{"id": "ana", "skills": ["till"], "max_hours": 40, "unavailable": ["sat-am"]}, ...], "shifts": [{"id": "sat-am", "start": "2026-09-12T08:00", "end": "2026-09-12T14:00", "required": 2, "skill": "till"}, ...], "rules": {"min_rest_hours": 11, "max_consecutive_days": 6}} returns {"assignments": [{"shift": "sat-am", "staff": ["ana", "ben"]}], "unfilled": [{"shift": "sun-pm", "short": 1}], "hours": {"ana": 30.0}, "solver_status": "OPTIMAL"}. The objective fills as many required slots as possible, then spreads hours evenly, then honours preferences (staff.prefer / staff.avoid shift ids). Use for weekly rotas of up to 60 staff and 150 shifts. Not a determination of labour-law compliance: the rules are the ones you pass. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "staff and shifts must be non-empty lists"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
validate
validate_problem
Check a problem's shape and obvious feasibility before spending solver time. FREE. Typical input {"type": "route", "problem": {"stops": [...], "matrix": [[...]], "vehicles": [...]}} returns {"ok": false, "issues": ["total demand 34 exceeds total capacity 30"], "size": {"stops": 14, "vehicles": 2}, "tier_hint": "route_plan_fleet (licence) - more than 12 stops"}. Types: route, pack, cut1d, cut2d, roster, knapsack; the problem object uses the same fields as the matching tool. Use first when an agent has assembled the problem from other data. Not a solve: it never calls the solver. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "type must be one of <value>"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Endpoints

URLTransportStateLatencyChecked
https://mcp.moltlinestudio.com/optimize streamable-http answering 521 ms 14 min ago

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Moltline Optimize — questions

Answers built from our own checks of this server.

What can Moltline Optimize do?
It exposes 11 tools, read directly from the server on our last check. Among them: cutting_stock_1d, cutting_stock_2d, distance_matrix_haversine, explain_solution, knapsack_select, pack_bins and 5 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 Moltline Optimize mostly used for?
Its tools cluster around pack, route and cutting. 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 Moltline Optimize 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 336 ms. The bar chart above shows every period we have measured.
How do I connect Moltline Optimize?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Moltline Optimize need an API key?
No. Moltline Optimize completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 11 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Moltline Optimize?
It answers our handshake in 336 ms on average, which is faster than 46% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
Is Moltline Optimize open source?
Yes — it is published under the MIT licence, written in Python and 0 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.