Retrieve a complete JSON example listing every accepted field for defining a new Abaqus simulation, so you can fill in the template and create a valid model specification.
Return an example simulation spec (JSON) showing every field the model authoring pipeline accepts. Fill this in to describe a new simulation.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.3.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It explicitly states the tool returns a JSON template and implies a read-only, non-mutating action through 'Return an example...'. It does not claim side effects or hidden behaviors, making it transparent for a zero-parameter getter.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences with no wasted words. The first sentence front-loads the action and output, and the second provides immediate usage guidance. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only getter with an output schema available, the description is fully sufficient. It states what is returned, that it is a fill-in template, and why an agent would use it. No additional context is needed for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so parameter semantics is not a source of difficulty. The schema documents the empty parameter set, and the description correctly avoids inventing parameters. A baseline of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Return') and a precise resource ('an example simulation spec (JSON)') and clarifies that it shows every field the model authoring pipeline accepts. It is clearly distinct from validation, building, and running tools, and the second sentence reinforces its role as a starting template.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use the tool: 'Fill this in to describe a new simulation.' It does not explicitly name alternatives or exclusions, so it falls short of a 5, but the intended usage is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.