aras-plm-mcp

aras_create_effectivity_model

Creates a product model to be used as an effectivity Model value (e.g., CP-40, CP-60) for defining product variants in PLM.

Instructions

Crea un modello di prodotto, usabile come valore della variabile Model dell'effettivita' (es. 'CP-40', 'CP-60').

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": {
      "nome": "<nome>"
    },
    "name": "aras_create_effectivity_model"
  }
}

Input Schema

Name	Required	Description	Default
`nome`	Yes
`etichetta`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 23, 2026

TDQS

B3.3/5.0

Behavior2/5

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

With no annotations provided, the description must disclose behavioral traits. It only states that it creates a model, with no mention of side effects, permissions, or response format. For a mutation tool, this is a significant gap—the agent has no indication of what happens beyond creation or whether any prerequisites exist.

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 a single, focused sentence with no unnecessary words. It front-loads the purpose and includes a concrete example, which is efficient and easy to parse for an agent.

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?

For a creation tool with no output schema and sparse annotations, the description is incomplete. It fails to explain the relationship between the model and effectivity beyond a brief mention, and does not describe input expectations or outcomes. An agent would struggle to know what to provide and what to expect.

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

Parameters1/5

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 by explaining the parameters. It does not mention 'nome' (name) or 'etichetta' (label) at all, leaving the agent without any understanding of their purpose or format. This is a critical omission for a tool with only two parameters.

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 a specific action: creating a product model, and provides a concrete use case (as the Model variable value for effectivity, with examples 'CP-40', 'CP-60'). This distinguishes it from other create tools (e.g., aras_create_item) by name and purpose.

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 gives clear context for when to use this tool: to create a model intended as an effectivity Model value. It implicitly differentiates from generic create tools, though it doesn't explicitly state when not to use it or mention alternatives. The example values add practical guidance.

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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