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Ansys CFX-MCP

Ansys CFX-MCP

Official
by ansys
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

validate_code

Validate CFX Python scripts in a dry-run mode to catch parse, type, and semantic errors before execution.

Instructions

Dry-run / validate CFX Python without applying side effects. Returns parse / type / semantic feedback.

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": {
      "code": "<code>"
    },
    "name": "validate_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYesPython source code submitted for validation, grounding, or execution.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description fully carries the behavioral burden. It discloses that it is a dry-run and has no side effects, which is critical. It also states it returns parse/type/semantic feedback, offering insight into the output.

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 that front-loads the key behavior with 'Dry-run / validate' and avoids any unnecessary 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?

For a simple one-parameter tool, the description is fairly complete. It explains the core behavior and output type, though it doesn't specify the exact structure of the feedback. Given no output schema, this is a minor gap.

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

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of the parameter with a description. The tool description adds the context of 'CFX Python' and the validation purpose, but the parameter semantics are largely defined by the schema already.

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 explicitly states the tool is for dry-run/validation of CFX Python and mentions it avoids side effects, which clearly distinguishes it from sibling run_code. It uses specific verbs and provides the resource 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 implies usage: use when you want to validate code without executing it, contrasted with run_code. It does not explicitly name alternatives or when-not-to-use, but the 'without applying side effects' gives clear context.

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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