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create_field_output_request

Define field output requests for a specific Abaqus step, specifying result variables and frequency to control when simulation data is recorded.

Instructions

Create a field output request for a specific step.

Args: variables: list of variable names, e.g. ["S", "E", "U", "RF"] frequency: output frequency (every N increments)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoF-Output-1
timeoutNo
frequencyNo
step_nameNo
variablesNo
model_nameNoModel-1

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only lists two arguments and does not mention side effects, prerequisites such as an existing model or step, validation behavior, or how the request is associated with the model.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with its purpose, but the Args block presents only two parameters while the schema has six, which is structurally misleading. It is under-specified rather than efficiently complete.

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

Completeness2/5

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

Even with an output schema present, the agent cannot determine how to target a specific step because step_name is undocumented, and model_name is also unexplained. The description is not complete enough for a six-parameter creation tool with no annotations.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate but only documents 2 of 6 parameters. It adds useful meaning for variables (with example list) and frequency ('every N increments'), but leaves name, timeout, step_name, and model_name completely unexplained.

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 states a specific verb and resource: 'Create a field output request for a specific step.' This clearly identifies what the tool does and distinguishes it from sibling create_history_output_request by the field-output focus.

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 by the purpose sentence—use this when you need a field output request for a step. However, it provides no explicit when-not-to-use guidance and does not mention alternatives like create_history_output_request or get_field_output_summary.

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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