Moltline Optimize

Route Plan

route_plan

Read-onlyIdempotent

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": ""} (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.

Input Schema

TableJSON Schema

Name	Required	Description
`depot`	No	index of the depot in stops (default 0).
`stops`	Yes	list of stop objects, depot first: id, optional demand.
`matrix`	Yes	square distance (or time) matrix, row i column j = cost from stop i to stop j.
`time_limit_s`	No	solver time budget in seconds (default 3, max 15).
`vehicle_capacity`	No	optional capacity in the units of demand (0 = unlimited).

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

Behavior4/5

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

Annotations already provide readOnlyhInt, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavior beyond that: the tool never raises a protocol error but returns a structured error object with corrective guidance, and it documents capacity-check behavior when demand and vehicle_capacity are used. No annotation contradiction exists.

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 purpose and a concrete example, then covers limits, use cases, exclusions, error behavior, and idempotency. Each section earns its place, and the length is justified for an optimization tool with non-obvious input expectations.

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?

With an output schema present, return values are already structured, yet the description still provides a illustrative response. It covers the stop limit, matrix constraints, depot handling, capacity checks, route_plan_fleet distinction, distance matrix sourcing, error behavior, and retry safety. Nothing essential is missing for correct invocation.

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 baseline is 3. The description genuinely adds meaning by explaining that the matrix must be square, the depot should be at index 0 unless the depot parameter says otherwise, units are user-defined, and optional demand plus vehicle_capacity triggers a capacity check. This goes beyond the schema.

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 description opens with a specific verb and resource: 'Order up to 12 stops into the shortest single-vehicle route on your distance matrix.' It distinguishes itself from route_plan_fleet by explicitly calling out single-vehicle routing, making the tool's scope unmistakable.

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

Usage Guidelines5/5

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

It provides explicit when-to-use guidance ('Use for one driver's day or a courier's loop') and explicit when-not-to-use guidance ('Not for several vehicles or time windows: use route_plan_fleet'), including a named alternative for distance input (distance_matrix_haver sin). This is strong routing to alternatives.

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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