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plan_simulation

Plan an OpenSees/quoFEM simulation from a natural language request; the tool infers parameters, asks clarifying questions when needed, and returns a complete execution plan.

Instructions

Plan an OpenSees/quoFEM simulation from the decision matrix.

Pass the facts you know; anything left None is inferred from the request only when unambiguous, and otherwise returned in open_questions for you to ask the user. The decision is the matrix's, not yours: do not override app_id, and compose the cells from the returned snippet, not from memory.

model_language: "tcl" | "python" parallelism: "serial" | "domain-decomposition" | "single-domain-parallel-solver" | "many-independent-cases"

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "request": "<request>"
    },
    "name": "plan_simulation"
  }
}

Input Schema

Name	Required	Description	Default
`n_cases`	No
`request`	Yes
`pipeline`	No
`parallelism`	No
`has_allocation`	No
`model_language`	No
`uq_or_calibration`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 12, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It reveals that None values are inferred only when unambiguous and otherwise surfaced as open_questions, warns against overriding app_id, and instructs the agent to compose cells from the returned snippet. This goes beyond generic safety hints and provides actionable behavioral context.

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 well-structured with a clear purpose statement, a concise behavioral contract, and a compact list of enum values. Every sentence adds value, and there is no repetition of schema fields that are already obvious from titles.

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?

Given the tool's complexity (7 parameters, output schema, sibling planning tools), the description covers the core usage rules, inference behavior, and key constraints. It could be more complete by explicitly naming when to use plan_simulation versus plan_calibration, but the provided context is sufficient for most planning scenarios.

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?

The schema provides 0% description coverage, so the description must compensate. It adds meaning by explaining that optional parameters are 'facts you know' and can be left None for inference, and it lists allowed values for model_language and parallelism. However, it does not explain the semantics of n_cases, pipeline, has_allocation, or uq_or_calibration, leaving gaps.

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 'Plan an OpenSees/quoFEM simulation from the decision matrix,' which names a specific verb, resource, and domain. It clearly distinguishes from sibling tools like plan_calibration by specifying simulation rather than calibration.

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?

The description explains how to invoke the tool ('Pass the facts you know...'), but it does not explicitly state when to choose this tool over alternatives such as plan_calibration. The context is clear, but no direct exclusion or comparison with sibling tools is provided.

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