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Moltline Optimize

Explain Solution

explain_solution
Read-onlyIdempotent

Plain-language summary of a solution from this server and the constraints that bind. FREE.

Typical input {"solution": } 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": ""} (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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
solutionYesthe result object returned by one of this server's solve tools.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive hints; the description adds value by explaining error behavior: it 'never raises a protocol error' and instead returns a structured error object. It also clarifies that input is recognized by fields across multiple solver tools, and that retrying after correction is safe.

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 longer than average but every sentence earns its place: purpose, cost, example, accepted input sources, use case, exclusions, error behavior, and retry guidance. Key information is front-loaded, with the core purpose stated first.

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?

Given only one parameter and rich annotations, this description is fully complete. It covers what the tool accepts, what it returns with a representative example, how errors manifest, and why retrying is safe. There is no missing information an agent would need to invoke it correctly.

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

Parameters5/5

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

The schema only says the `solution` parameter is a result object from a solve tool. The description substantially expands this by naming the exact solver tools whose outputs are accepted, providing a concrete example input and output, and explaining that the object is recognized by its fields.

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: 'Plain-language summary of a solution from this server and the constraints that bind.' It clearly differentiates itself from solve tools by stating 'Not a re-solve' and by listing the exact solver result types it recognizes.

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?

The description explicitly states when to use it: 'Use to turn solver output into a message for a dispatcher or a shop floor.' It also gives a clear exclusion, 'Not a re-solve,' and tells the agent that invalid input yields a fixable error so retry is safe.

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.

Resources