Moltline Optimize

Cutting Stock 2D

cutting_stock_2d

Read-onlyIdempotent

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": ""} (for example {"error": "sheets and parts must be non-empty lists ( and )"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema

Name	Required	Description
`kerf`	No	saw blade width lost per cut.
`grain`	No	true when parts must keep their orientation (l along the sheet's l).
`parts`	Yes	{id, l, w, qty, rotate}; rotate overrides the grain rule per part.
`sheets`	Yes	{id, l, w, qty}; used in the order given.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 6, 2026

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint; the description reinforces these and adds a detailed error contract: invalid input never raises a protocol error but returns an error object with guidance. It also discloses the heuristic nature, the guillotine-cut constraint, and the grain/rotate override behavior. This goes well beyond the structured annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core function, then uses a compact example to convey input/output shape, followed by constraints, use cases, limitations, and error handling. Every sentence contributes distinct information, and the length is justified by the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a rich output schema, so return-value details do not need to be repeated. The description covers the key operational facts: read-only and idempotent behavior, error handling, optimality caveat, guillotine constraint, and grain semantics. Nothing essential for an agent to select and invoke the tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents each parameter. The description adds concrete meaning through the typical input example, including kerf as a numeric value, parts with quantities, and the resulting layout structure. It also explains the grain rule and per-part rotate override, which adds behavioral context to the parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence states the operation and resource: building guillotine cut layouts for rectangular parts from sheets, with kerf and grain. The 'guillotine' and '2D' qualifiers distinguish it from siblings like cutting_stock_1d and pack_bins. Example input/output further clarifies the exact scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly names target use cases: cabinet, sign, and sheet-metal cut lists. It also flags the tool as a best-fit shelf heuristic and 'not proven optimal,' signaling when it may not be suitable. It does not explicitly name sibling alternatives or exclusions, but the usage context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.5/5.0

Disambiguation5/5

Each solver targets a distinct optimization domain (cutting, packing, routing, knapsack, rostering), and scale variants like pack_bins/pack_bins_large and route_plan/route_plan_fleet are explicitly differentiated by size/scope. Helper and meta tools (distance_matrix_haversine, validate_problem, explain_solution) are clearly separate from solve tools.

Naming Consistency4/5

Names are uniformly lowercase snake_case and organized into recognizable families such as cutting_stock_1d/2d, pack_bins/pack_bins_large, and route_plan/route_plan_fleet. The pattern is not strictly verb_object throughout—knapsack_select and distance_matrix_haversine are noun-first or descriptive—so there is minor inconsistency.

Tool Count5/5

With 11 tools, the server is well-scoped for an optimization toolkit: one core solver per problem type, a large-scale packer variant, a distance-matrix helper, and validation/explanation tools. Each tool has a clear role, and the count supports broad coverage without becoming bloated.

Completeness4/5

The server covers a complete solve-validate-explain workflow across major OR problem types—cutting, packing, routing, knapsack, and rostering. Minor gaps exist, such as pack_bins_large not being listed as recognized by explain_solution, and some features are explicitly documented exclusions rather than missing functionality.

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