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simulate_mrgsolve

Run pharmacokinetic simulations using mrgsolve R package without requiring NONMEM. Provide model code or file to generate simulated data for pharmacometric analysis.

Instructions

Run a simulation using mrgsolve (R). No NONMEM needed. Provide model code or model file path.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_codeNoInline mrgsolve model code
model_fileNoPath to mrgsolve .mod file
data_pathNoPath to dataset for simulation (optional)
n_subjectsNoNumber of subjects (default: 100)
end_timeNoEnd time for simulation (default: 24)
deltaNoTime step (default: 0.5)
dose_amtNoDose amount (for simple dosing regimen)
dose_cmtNoDosing compartment (default: 1)
seedNoRandom seed (default: 12345)
output_pathNoPath to save output CSV
Behavior2/5

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

No annotations are provided, so the description carries full burden. It fails to disclose what the tool returns (given no output_schema), whether it creates/modifies files via output_path, or computational intensity. The description only states what it does, not behavioral side effects.

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?

Three sentences with zero waste: purpose declaration first, sibling differentiation second, input requirement third. Appropriately front-loaded and compact for the complexity level.

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

Completeness3/5

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

With 10 parameters and no output schema, the description should disclose return behavior or file output characteristics. While the schema documents parameters well, the agent lacks guidance on what simulation results look like or how output_path behaves.

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?

With 100% schema coverage, baseline is 3. The description adds crucial semantic guidance that either model_code OR model_file must be provided—a logical requirement not enforced by the schema since both parameters are marked optional. This prevents invocation errors.

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 clearly states the specific action (Run a simulation) and resource (mrgsolve), and explicitly distinguishes from NONMEM-based siblings with 'No NONMEM needed.' The R context is also specified.

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 'No NONMEM needed' phrase effectively signals when to use this tool versus NONMEM-based alternatives like submit_run or execute_psn_bootstrap. However, it lacks explicit guidance on when to use this versus translate_to_mrgsolve or prerequisites for the simulation.

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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