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SatisfactoryMCP

by lukszi
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Python
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trace_upstream

Trace what feeds a machine or what it feeds by walking a Satisfactory save's connections, revealing dependencies for cutover and repiping.

Instructions

What feeds a machine, or what it feeds -- walked on the save's own connections.

factory_query answers this between LABELLED sets. This answers it for one machine or one building type, which is the question a cutover actually asks: thirteen Oil Extractors sit on the Spire nodes and twenty Fuel Generators are burning, and repiping the wrong extractor first drops several GW.

Direction is READ, not guessed. Every material edge carries its connector role, and 92.5% of the connectors landing on a machine name their direction outright; the rest are all on extractors or generators, whose own nature settles them. Where even that fails the edge is walked BOTH ways -- over-reporting a feeder is recoverable, missing one is not.

Belts and pipes are walked THROUGH and left out of the table: a trace from the generators touches 331 nodes at depth 72, nearly all of it conveyor. What the route crossed is named instead in a note -- how many runs of each medium, and the ids search_conduits takes for the ones that have them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
saveNo
seedYesa machine instance, a factory label, or a building name
as_ofNopin to one world state: a sav:… token from an earlier answer
limitNomax rows (hard cap 25)
worldNo
directionNoup (what feeds it) | down (what it feeds)up

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / as_of
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "pin to one world state: a sav:… token from an earlier answer",
+  "title": "As Of"
+}
  2. First observedv0.1.0

TDQS

A4.1/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so richly: direction is 'READ, not guessed', 92.5% of connectors name their direction, and ambiguous edges on extractors/generators are walked BOTH ways with an explicit rationale ('over-reporting a feeder is recoverable, missing one is not'). It also discloses that belts/pipes are walked through and excluded from the table, with medium counts and search_conduits ids given in a note.

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-loads the core purpose in the first clause and the factory_query contrast immediately after. The prose is dense and stylized, and the 331-node/depth-72 example is somewhat illustrative rather than operational, but each sentence contributes behavioral detail rather than filler.

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

Completeness4/5

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

For a 6-param tool with no annotations and no output schema, the description supplies strong behavioral context, including that output is tabular ('left out of the table') and that a supplementary note carries conduit info. It leaves parameter-level meaning for save/world/as_of/limit uncovered, but the trace semantics themselves are well covered.

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

Parameters3/5

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

Schema coverage is 67%, so several params (save, world, as_of, limit) are unspecified in both places. The description conveys the meaning of the direction choice ('what feeds a machine, or what it feeds') and the granularity of the seed ('one machine or one building type'), but adds no syntax or format detail for the remaining parameters. Baseline 3 fits.

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?

States a specific operation (walking the save's own material connections) and explicitly contrasts its scope with factory_query: 'factory_query answers this between LABELLED sets. This answers it for one machine or one building type.' That lets an agent route between the two. The only minor ambiguity is that the name says 'upstream' while the description covers both directions, resolved only by the direction param.

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

Usage Guidelines4/5

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

Gives clear context for when to pick this over factory_query (single machine or building type vs labelled sets) and frames the real-world question it answers (cutover ordering). It does not explicitly state when not to use it, but the alternative comparison is concrete enough to guide selection.

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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