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Glama
Factorio LLM Use
by Tsyklop

craft

Queue manual item production using real ingredients and game time, returning an action ID for tracking.

Instructions

Queue hand crafting using real ingredients and game time. Returns an action ID.

Input Schema

NameRequiredDescriptionDefault
countNo
recipeYes
qualityNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

C2.9/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose two useful traits: the craft consumes real ingredients and advances game time (i.e., it is not instantaneous). Mentioning an action ID implies an asynchronous queue, but it never says to poll get_action_status or that cancel_action can abort it, and says nothing about failure modes or permissions.

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?

Two short sentences, front-loaded with the action and followed by the return contract, with zero padding. It is efficient, though the brevity is part of why semantics are under-specified.

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?

An output schema exists, so return values need not be spelled out, but for a mutation-like queue operation with no annotations and 0% parameter coverage the description is too thin. Recipe format and quality semantics are the biggest unanswered questions an agent would need before calling it correctly.

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 0% across three parameters. The description never explains what form `recipe` takes (prototype name string? object?), what `quality` means or that null is the default, or how `count` interacts with ingredient consumption. None of the meaning missing from the schema is supplied.

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 gives a specific verb plus resource ("queue hand crafting") and distinguishes it implicitly from machine-based production by specifying hand crafting. An agent can tell what it does at a glance, though it never names a sibling such as list_recipes (for recipe identifiers) or configure_entity (for machine crafting).

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no prerequisites (e.g., needing ingredients in inventory, needing to look up recipe names via list_recipes first), and no statement of when to prefer this over machine crafting. The agent must infer the whole selection context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.