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Moltline Optimize

Validate Problem

validate_problem
Read-onlyIdempotent

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": ""} (for example {"error": "type must be one of "}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

NameRequiredDescriptionDefault
typeYesroute, pack, cut1d, cut2d, roster or knapsack.
problemYesthe same object you would pass to the tool (stops/matrix/vehicles, items/containers, ...).

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent/non-destructive, and the description adds real context beyond them: it is FREE, never raises a protocol error, returns a {"error": ...} object instead, and is safe to retry after correction. This is exactly the supplementary behavioral detail the annotations cannot express.

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

Conciseness4/5

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

Front-loaded with purpose and cost, then examples and error behavior. The worked example is somewhat verbose but each line (input shape, return shape, types, error format) carries actionable information, so little is wasted.

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 a nested-object input and an output schema, the description still supplies concrete return-field semantics (ok, issues, size, tier_hint) and explicit error behavior, which is more than enough 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 coverage is 100% so the baseline is 3, but the description adds a concrete typical input example and explains that the problem object uses the same fields as the matching tool, which meaningfully clarifies how to populate the nested 'problem' parameter. That is a step above what the schema alone conveys.

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?

States a specific action on a specific resource ('Check a problem's shape and obvious feasibility') and explicitly disclaims what it is not ('Not a solve: it never calls the solver'), cleanly separating it from all the solver siblings. An agent can tell exactly what this does without opening the schema.

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?

Gives an explicit trigger ('Use first when an agent has assembled the problem from other data') and routes the agent onward via tier_hint to the matching sibling (e.g. route_plan_fleet). Both the when and the next step are named, leaving nothing to inference.

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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