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optimize_production_plan_tool

Optimize multi-period production planning by balancing product costs, resource constraints, and demand across periods to maximize profit or minimize costs. Accepts inventory and setup costs, with solver options for efficient planning.

Instructions

Optimize multi-period production planning to maximize profit or minimize costs.

    Args:
        products: List of product dictionaries with costs and resource requirements
        resources: List of resource dictionaries with capacity constraints
        periods: Number of planning periods
        demand: List of demand requirements per product per period
        objective: Optimization objective ("maximize_profit", "minimize_cost", "minimize_time")
        inventory_costs: Optional inventory holding costs per product
        setup_costs: Optional setup costs per product
        solver_name: Solver to use ("CBC", "GLPK", "GUROBI", "CPLEX")
        time_limit_seconds: Maximum solving time in seconds (default: 30.0)

    Returns:
        Optimization result with optimal production plan
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
productsYes
resourcesYes
periodsYes
demandYes
objectiveNomaximize_profit
inventory_costsNo
setup_costsNo
solver_nameNoCBC
time_limit_secondsNo
Behavior2/5

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

No annotations are provided, so the description carries full burden. It does not disclose whether the tool is read-only, destructive, requires authentication, or has side effects. The return value is vaguely described without detail.

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?

The description is concise, front-loaded with purpose, and lists parameters efficiently. However, it could be more structured with clearer separation of purpose and usage.

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

Completeness3/5

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

Given the tool's complexity and lack of output schema/annotations, the description provides parameter explanations but lacks detailed return format or examples. It feels incomplete for an AI agent to fully understand the result.

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?

Despite 0% schema description coverage, the description's Args section adds meaning to each parameter, e.g., 'products: List of product dictionaries with costs and resource requirements.' This compensates well for the bare 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?

Description clearly states the tool optimizes multi-period production planning to maximize profit or minimize costs. This specific verb and resource distinguish it from sibling optimization tools that solve other types of problems.

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

Usage Guidelines3/5

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

The description implies usage for production planning but does not explicitly state when to use it versus alternatives like solve_linear_program_tool. No exclusions or context are provided.

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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