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sim_compare

Destructive

Run several scenarios against one model on one shared seed and return them side by side — the seed sharing is server-enforced, so differences are the scenarios, not the dice. Returns a summary by default (finals, throughput/mean/P95 metrics, contention, depletion — no time series); pass full=true for the complete trajectories, which run to hundreds of KB. A scenario carrying "summary": true stays summarized even under full=true, so one comparison can chart some scenarios and only read the rest. Unset hours default to 8, samples to 60 (the trajectory grid, which only matters under full=true — metrics are time-weighted and do not depend on it) and realizations to 16 per scenario. Each scenario can set its own "engine" (see sim_scenario / docs/engine-selection.md); comparing an "ode" run against an "ssa" one is legitimate but the shared seed only removes dice from scenarios using the same engine.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesmodel id
fullNoinclude the sample-grid time series in every result (large; default false); a scenario with its own "summary": true is left summarized regardless
scenariosYesJSON array of scenarios, each with a name, e.g. [{"name":"today","hours":8},{"name":"one more","hours":8,"marking":{"staff":3}},{"name":"bigger batches","hours":8,"params":{"batch_size":6}}]

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

The description discloses rich behavioral details beyond the annotations: server-enforced shared seed, summary vs full output sizes, metrics included, time-weighted sampling behavior, scenario-level summary override, and engine-dependent seed effects. The annotations already flag destructive/open-world characteristics, lowering the burden on the description.

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 dense but every sentence earns its place, covering purpose, key caveats, defaults, and engine rules without fluff. The main behavior is front-loaded, and the more technical constraints follow naturally.

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?

With no output schema, the description compensates by naming the default summary metrics, warning about full output size, listing defaults, and clarifying seed/engine behavior. An agent has enough information to invoke the tool correctly and interpret what it returns.

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

Parameters5/5

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

Even though schema coverage is 100%, the description adds substantial meaning: defaults for hours, samples, and realizations; the full=true consequence of large trajectories; the interplay with scenario-level 'summary': true; and per-scenario engine selection. This goes far beyond the schema's one-line parameter descriptions, especially for 'full' and 'scenarios'.

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 opening sentence states a specific operation: 'Run several scenarios against one model on one shared seed and return them side by side.' This clearly identifies the tool's purpose and distinguishes it from scenario-creation or pipeline tools like sim_scenario and sim_run_pipeline.

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 strong usage context: default summary output, when to pass full=true, per-scenario summary override, default hours/samples/realizations, and legitimate cross-engine comparisons. It does not explicitly name alternative tools or exclusions, but it provides enough context for an agent to use it correctly.

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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