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COMSOL MCP Server

COMSOL MCP Server

by Zhangyoupeng1996
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

model_inspect

Explore the structure and contents of a COMSOL model, including parameters, physics, and studies. Get detailed insights to understand model composition.

Instructions

Get detailed information about a model's structure and contents.

Args: model_name: Name of the model to inspect (default: current model)

Returns: Detailed model structure including parameters, physics, studies, etc.

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": "model_inspect"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_nameNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are provided, so the description must disclose all behavioral traits. It mentions what is returned (parameters, physics, studies) but does not state that the tool is read-only or safe to call without side effects. This omission is a gap for a tool that likely does not modify state.

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 concise, uses a standard docstring format with Args and Returns, and front-loads the core purpose. Every sentence contributes value with no wasted words.

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

Completeness4/5

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

Given the tool's simplicity (1 optional parameter) and no output schema, the description provides sufficient context about input and return values. It covers the essential behavioral aspects, though it could explicitly state the read-only nature.

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 coverage is 0%, but the description adds meaning by stating the default value ('current model') and explaining the parameter's role ('Name of the model to inspect'). This compensates well for the lack of schema detail.

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 the tool's purpose with a specific verb ('Get') and resource ('detailed information about a model's structure and contents'), which distinguishes it from sibling tools like model_create, model_list, or model_load.

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?

The description implies usage for inspection tasks but lacks explicit guidance on when to use this tool versus alternatives (e.g., model_list for listing models, physics_list for physics info). No 'when not to use' or alternative names are provided.

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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