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simulate

Run time-step simulations of water systems with timed set-point or ramp events, returning downsampled min, max, and final series along with warnings and mode changes.

Instructions

Simulate over time with a fixed step and timed events (set or linear ramp).

Samples at every multiple of step, at each event time and at the end. Returns downsampled series with min, max and final over the full run; each warning once (first time, last time, active at end); every mode change. Controls act after each sample's solve (taking effect over the next step); their series are always included and controls reports each loop at the end. Unless restore is true, the system keeps its final state.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`step`	No	Time step, e.g. '1 s'.	1 s
`units`	No	Unit per path, e.g. {'v.volume_flow': 'L/s', 'mains.port.p': 'bar absolute'}; a key '*.<name>' matches every path ending in '.<name>'.
`events`	No	e.g. [{'at': '60 s', 'set': {'valve.opening': 0}}, {'at': '2 min', 'ramp': {'filter.clogging': [0, 0.8]}, 'over': '30 min'}].
`restore`	No	Put parameters, inputs and states (tank levels) back afterwards.
`duration`	Yes	Total time: seconds or e.g. '10 min'.
`system_id`	Yes
`variables`	No	Paths ('v.volume_flow', 'v.port_a.p'), instance names, 'control' or '*'. Default: observables, states, port pressures and control results.
`max_points`	No	Downsample series to this many samples.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`time`	Yes
`issues`	Yes
`samples`	Yes
`controls`	No
`duration`	Yes
`warnings`	Yes
`system_id`	Yes
`variables`	Yes
`final_modes`	Yes
`mode_changes`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 24, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations indicate readOnlyHint=false (mutation allowed) and destructiveHint=false. The description adds critical context: it mentions the system keeps its final state unless restore is true, and details the exact output behavior (downsampled series, min/max/final, warnings once, mode changes, control series). This goes beyond annotations and fully discloses side effects and return semantics.

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 about 100 words and every sentence delivers essential information. It leads with the core purpose, then details sampling, output, and side effects. No redundancy or filler; each clause is necessary.

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?

Given the complexity of an 8-parameter simulation tool with events, controls, and state restoration, the description covers all essential behavioral aspects: sampling schedule, output contents, control semantics, side effects, and restoration option. The output schema exists (implied by 'returns downsampled series'), and the description aligns with it. No critical gaps.

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 88%, so most parameters already have descriptions. The description adds value by explaining behavioral semantics: 'Samples at every multiple of step, at each event time and at the end' clarifies how step and events are processed, and 'controls reports each loop at the end' explains the control output. This enriches understanding beyond the schema.

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 explicitly states the tool simulates over time with a fixed step and timed events, clearly distinguishing it from siblings like `solve` or `solve_for` which likely handle steady-state or targeted solves. The verb 'Simulate' plus resource 'over time' makes the purpose unambiguous.

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 clearly indicates this is for time-based simulation with step and events, which implies the use case. However, it does not explicitly name alternatives or state when not to use it, unlike the calibration example that specified 'To filter by user/workspace, use search_calls_extensive instead.' Still, the intent is clear enough for an agent to select it appropriately.

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