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kompas3d

kompas3d

by hymaxo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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list_variables

Retrieve user-defined variables and driving expressions from a KOMPAS-3D model. Specify a feature ID to get its native driving variables and equations.

Instructions

Read user variables or a specific native feature's driving variables and expressions.

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": {
      "document_id": "<document_id>"
    },
    "name": "list_variables"
  }
}

Input Schema

NameRequiredDescriptionDefault
feature_idNo
document_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. 'Read' appropriately signals a non-mutating operation, but it does not disclose return shape, behavior when feature_id is omitted, or any limitations. This is adequate but minimal.

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 sentence with no filler or redundancy. The main verb, object, and optional scoping condition are all front-loaded.

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

Completeness3/5

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

For a simple two-parameter read operation, this is adequate but not complete. There is no output schema and the return value is not described, and the optional feature_id behavior is only implied by the word 'or' rather than explicitly stated.

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?

Schema description coverage is 0%, so the description must compensate. It does by mapping feature_id to 'a specific native feature' and implying that omitting it means user variables. document_id is not described, but its purpose is reasonably clear from the parameter name.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a clear verb ('Read') and identifies the resource: user variables or a native feature's driving variables and expressions. It does not explicitly distinguish itself from sibling tools like set_variable, but the read-vs-write contrast is implicitly clear.

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 provides clear context: use this tool to read variables, either at the user level or for a specific native feature. It does not state exclusions or name alternatives, so it misses the top score, but the intended usage is not left to inference.

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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