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SCModeling

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run_simulation

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Run a supply-chain simulation on a bundled SCModeling sample model (sdi-db). Returns metrics, inventory time-series, orders, shipments, routing and BOM. ANTI-FABRICATION: the returned numbers come from a real discrete-event simulation run on the sc-sim engine. Quote them VERBATIM in your reply. Do not round, estimate, average, or compute derived figures from training-data recall. If the user asks a follow-up about the same model, re-call this tool rather than recalling numbers from earlier in the conversation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesWhich sample model to simulate

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailsNoFull run detail: config, locations, materials, routing, demands, orders, shipments, inventory_timeseries
metricsNoTop-line scalar KPIs (orders, shipments, simulation_days)
metadataNoModel name, version, timestamp

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / model_id / enum
      Previous value: -[
-  "simple-sc-demo",
-  "cookie-making",
-  "supply-chain-demo"
-]New value: +[
+  "simple-sc-demo",
+  "cookie-making"
+]
  2. Changed1 schema field changed
    • changedInput schema / properties / model_id / enum
      Previous value: -[
-  "simple-sc-demo",
-  "cookie-making",
-  "supply-chain-demo",
-  "bottling-line-fitted"
-]New value: +[
+  "simple-sc-demo",
+  "cookie-making",
+  "supply-chain-demo"
+]
  3. Changed1 schema field changed
    • changedInput schema / properties / model_id / enum
      Previous value: -[
-  "simple-sc-demo",
-  "cookie-making",
-  "supply-chain-demo"
-]New value: +[
+  "simple-sc-demo",
+  "cookie-making",
+  "supply-chain-demo",
+  "bottling-line-fitted"
+]
  4. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations, the description discloses that results come from a real discrete-event simulation run, that they must be quoted verbatim without rounding or derivation, and that repeated calls are preferred over memory. This is valuable behavioral context that annotations alone do not provide.

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?

Every sentence earns its place: purpose and returns, anti-fabrication rule, and follow-up policy. The core action is front-loaded, and the behavioral instructions are directly relevant to correct tool use.

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?

For a one-parameter tool with an output schema and annotations, the description covers invocation and response handling adequately. The anti-fabrication guidance is especially important for an LLM agent to produce faithful, non-hallucinated answers.

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 100% for the single model_id parameter, and the schema includes an enum and a description. The description adds only the context of a bundled SCModeling sample model, which does not change the parameter-semantics baseline.

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?

The description opens with a specific verb and resource: 'Run a supply-chain simulation on a bundled SCModeling sample model (sdi-db).' It also lists the returned data types, which clearly distinguishes this tool from sibling list/describe/explain/get tools. The role of executing a simulation is unmistakable.

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?

The description gives clear context for when to use the tool: whenever simulation numbers are needed, and it explicitly instructs re-calling on follow-ups rather than recalling numbers. It does not explicitly contrast with sibling result-fetching tools, but the anti-fabrication directive strongly implies this tool is the authoritative source for simulation outputs.

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

All SCModeling toolsInspect
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