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FelipeDiazAimar

FlexSim MCP Server

by FelipeDiazAimar
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flexsim_run_experiments

Run FlexSim parameter sweeps across scenarios and seeds, execute each replication, then write summary.csv and manifest.json for batch what-if analysis.

Instructions

Run a parameter sweep with replications and write summary.csv + manifest.json (Batch MVP).

For each scenario x seed: reset -> apply Model.parameters -> randinit(Source, seed)
-> run_to_time -> get_statistics. Seeds only diverge in the window backend
(Python ignores randinit). Never touches PerformanceMeasureTables.

Args:
    scenarios: [{name, params: {Rate: 5}}] (params go to Model.parameters.<name>).
    seeds: e.g. [101, 202, 303].
    run_to_time: simulated seconds.
    run_speed: Run Speed slider (window only, default 1000).
    statistics: {objects, metrics} (default content/input/output basics).
    artifact_dir: new directory for summary.csv + manifest.json.
    stop_on_error: abort on first failing replica.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full load and does so well: it discloses that summary.csv and manifest.json are written to a new directory, that PerformanceMeasureTables are never touched, that randinit only diverges in the window backend (Python ignores it), and that stop_on_error aborts on the first failing replica. It stops short of describing failure behavior when stop_on_error is false or any permission/auth requirements.

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-loaded purpose sentence, then a compact workflow trace, then a scannable Args list. Every line earns its place, with the backend-divergence and PerformanceMeasureTables notes being genuinely load-bearing caveats 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 complex batch tool with an output schema (so return values need not be explained), the description covers the execution pipeline, artifact outputs, and every parameter. The only gap is the absence of guidance on choosing this tool over the other experimenter/run siblings, which the usage dimension already penalizes.

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?

Schema description coverage is 0% at the property level, so the description must compensate, and it does: every argument is documented with added meaning beyond types — params map to Model.parameters.<name>, run_speed is window-only with default 1000, statistics defaults to content/input/output basics, artifact_dir must be a new directory. It does not restate schema constraints like the 20-seed cap, but the semantic coverage is strong.

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 opens with a specific verb+resource+scope: 'Run a parameter sweep with replications and write summary.csv + manifest.json (Batch MVP).' The artifact-writing and sweep semantics clearly distinguish it from siblings like flexsim_run and flexsim_run_to_time, though no alternative tool is named explicitly, keeping it 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?

Usage is implied through the workflow description and the 'Batch MVP' label, which suggests batch execution, but there is no explicit when-to-use versus flexsim_experimenter_run, flexsim_experiment_plan, or plain flexsim_run. The agent must infer the selection criterion.

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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