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Compute total raw and intermediate resource rates per minute for any Satisfactory item. Uses LP to solve recipe cycles and alternates, naming the chosen recipe per component.

Instructions

Flattened bill of materials: total raw and intermediate rates for qty/min of an item.

qty is a RATE, per minute. Solved by the LP, never by expanding the recipe tree: Recycled Plastic and Recycled Rubber form a real 2-cycle, so an expansion has no correct depth limit. Every row names the recipe chosen for that item, because alternates change the totals materially.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qtyNo
itemYes
saveNo
limitNomax rows (hard cap 25)
worldNo
outletsNo
allow_sinksNo
only_recipesNo
exclude_recipesNo
Behavior4/5

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

With no annotations, the description carries a heavy burden. It discloses key behaviors: qty is a rate per minute, the output is computed via LP rather than tree expansion, and every row names the recipe chosen. This adds meaningful context beyond the schema. However, it omits other behavioral details like output format, pagination, or error conditions, so it is not fully transparent.

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 concise and well-structured. The first sentence states the purpose and output, the second adds crucial behavioral context about the algorithm and recipe independence. Every word earns its place, and there is no filler or redundant content.

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

Completeness2/5

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

This is a complex tool with 9 parameters and no output schema. The description provides some critical context (the LP algorithm and the cycle problem) but is otherwise incomplete. It does not explain the parameters beyond qty, or describe the return structure, which is essential for a tool of this complexity. The description covers the 'why' but not the 'what' of inputs/outputs.

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

Parameters2/5

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

Schema description coverage is only 11%, and the description only clarifies the 'qty' parameter (stating it is a rate per minute). The other eight parameters (save, limit, world, outlets, allow_sinks, only_recipes, exclude_recipes) are left unexplained in both the schema and description. The description does not compensate for the low schema coverage, so the parameter semantics score must be low.

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

Purpose4/5

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

The description clearly states the tool's function: 'Flattened bill of materials: total raw and intermediate rates for qty/min of an item.' This is a specific and informative purpose statement. However, it does not explicitly distinguish it from sibling tools like trace_upstream or recipe_detail, so it falls short of a 5.

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 when to use this tool by explaining that it solves via LP and avoids expanding the recipe tree due to real cycles (Recycled Plastic and Recycled Rubber). This gives context about suitable scenarios but does not explicitly state alternatives or when not to use it, so it offers implied rather than explicit usage guidance.

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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