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aras-plm-mcp

aras-plm-mcp

by Erryb95
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

aras_list_dashboards

Lists all dashboards configured in Aras with the content types they host. Find available dashboards like Engineering Efficiency or Time To Manufacturing for your PLM work.

Instructions

Cruscotti configurati in Aras, con i tipi di contenuto che ospitano. Su un'istanza standard ne esistono gia' diversi (Engineering Efficiency, Time To Manufacturing...).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "aras_list_dashboards"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the output includes content types and that typical instances contain standard dashboards, giving some insight into the result. However, it does not describe the return format, ordering, pagination, or any potential limitations, so transparency is incomplete but not misleading.

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 highly concise with two sentences: the first states the core purpose, and the second adds relevant context about typical instances. It is front-loaded with the main function and contains no unnecessary words or redundant information.

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

Completeness5/5

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

For a simple no-parameter, no-output-schema tool, the description is complete. It explains what the tool returns (dashboards with content types) and provides typical examples, which is sufficient for an agent to know what to expect. There is no missing critical information.

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?

The tool has zero parameters, so there is no parameter semantics to explain. The baseline for zero parameters is 4, and the description does not need to add parameter-specific meaning. It does not miss anything because there are no parameters to clarify.

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 it lists configured dashboards ('Cruscotti configurati in Aras') and the content types they host, which is specific and distinguishes it from siblings like aras_list_reports or aras_list_item_types. The verb 'list' is implied by the name and reinforced by the description, so an agent can easily understand the tool's purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly indicate when to use this tool over alternatives, nor does it name any sibling tools or provide exclusion criteria. It only mentions that standard instances have several dashboards, which gives context but no direct guidance on selecting this tool versus others. This falls short of the explicit when/when-not guidance expected.

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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