Moltline Optimize MCP server

• cutting_stock_1dFree

  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_2dFree

  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.

• distance_matrix_haversineFree

  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_solutionFree

  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_selectFree

  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.

• pack_binsFree

  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_largeFree

  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.

• roster_shiftsFree

  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.

• route_planwrite actionFree

  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_fleetFree

  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.

• validate_problemFree

  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.

• –Code scanremote-only server, no package to scann/a
• Live reliabilityremote reachable in 392ms20/20
• Tool poisoning11 tool descriptions checked15/15
• Auth qualityopen endpoint exposes 1 write-action tools with no auth3/15
• Maintenancelast push 0 days ago15/15
• Maintainer identitynamespace and repository owner differ; website matches verified namespace5/10

Moltline Optimize: common questions

Is Moltline Optimize MCP server safe?

Mostly: it is graded B (77/100). Read the Moltline Optimize safety report

How do I install Moltline Optimize?

It runs remotely at mcp.moltlinestudio.com. Add it to Claude Code, Claude Desktop or Cursor with the snippets above, or call it through the mcp.market gateway without installing anything.

Does Moltline Optimize need an API key?

Not as far as the registry entry and our scan can tell: no credentials are declared or required.

Is Moltline Optimize maintained?

The last commit was in the last day (2026-09-19). The latest release is v1.2.0.

Is Moltline Optimize up?

100% of our last 3 checks got an answer. We check remote servers about four times a day.

What can I use instead of Moltline Optimize?

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