COMSOL MCP Server

by Zhangyoupeng1996

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results_global_evaluate

Evaluate global expressions like integrations, maxima, or derived values to return a single numerical result from a COMSOL model.

Instructions

Evaluate a global expression (returns a single scalar value).

Common global expressions include:

Integration: "intop1(T)" where intop1 is an integration operator
Maximum: "maxop1(T)"
Derived values: "2*es.intWe/U^2" for capacitance

Args: expression: Global expression to evaluate unit: Desired unit for result dataset: Dataset name model_name: Model name (default: current model)

Returns: Single numerical value

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": {
      "expression": "<expression>"
    },
    "name": "results_global_evaluate"
  }
}

Input Schema

Name	Required	Description	Default
`expression`	Yes
`unit`	No
`dataset`	No
`model_name`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 25, 2026

TDQS

B3.3/5.0

Behavior2/5

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

With no annotations, the description should disclose behavioral traits. It only states basic operation (evaluate and return scalar) without mentioning side effects, permissions, rate limits, or prerequisites like solving the model first.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: purpose, examples, args list, returns. It is concise with no redundant phrases, though the args list could be slightly more compact.

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?

The description covers purpose, examples, and parameter meanings. However, it lacks details on error scenarios, prerequisites (e.g., model solved), or whether the tool is read-only, which are important for complete understanding.

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%, so description must add meaning. It provides brief but informative descriptions for each parameter (unit, dataset, model_name with default) beyond the schema, though could offer more detail like unit formats.

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 clearly states the tool evaluates a global expression returning a scalar value, with examples. It specifies 'global' to distinguish from similar tools like results_evaluate, though not explicitly contrasting with siblings.

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 computing global expressions but does not provide explicit when-to-use or when-not-to-use guidance, nor mentions alternatives like results_evaluate for non-global evaluations.

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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