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Ansys CFX-MCP

Official
by ansys

connect

Establish a connection to a CFX backend for simulation workflows. Choose a backend kind or auto-select, and pass backend-specific options like URL or token to enable CFX-Pre, Solver, and CFD-Post operations.

Instructions

Connect the cfx leaf to a backend. Available backend kinds: ['pycfx']. Pass backend_kind to choose, or omit it to auto-select. Backend-specific options (url/token/ip/port/...) go in connect_kwargs as a dict and are forwarded to the backend.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
backend_kindNoBackend kind to connect, or ``None`` to use the default backend. Default is ``None``.
connect_kwargsNoBackend-specific connection options forwarded to the selected backend. Default is ``None``.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
messageNo
endpointNo
candidatesNo
error_codeNo
backend_kindNo
Behavior3/5

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

With no annotations, the description carries the transparency burden. It discloses backend kinds, auto-selection behavior, and kwargs forwarding, but it doesn't mention side effects such as whether an existing connection is replaced, whether authentication is needed, or error behavior. This is moderate transparency with notable gaps.

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?

Three concise sentences lead with the core purpose, then cover backend selection and option forwarding. No filler or redundant restatement of schema fields.

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?

The tool is simple with two optional parameters, an output schema, and full schema coverage. The description sufficiently explains both parameters and the overall connect workflow, though it could briefly note prerequisites or state implications.

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 100%, so baseline is 3, but the description adds meaning: it lists the available backend kind ('pycfx'), explains that omitting it triggers auto-selection, and clarifies that 'connect_kwargs' is a dict of forwarded backend-specific options like url/token/ip/port. This adds real value beyond the schema.

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 action: 'Connect the cfx leaf to a backend,' naming both the resource and the target. It also distinguishes this from sibling tools like disconnect and session_status by focusing on establishing a connection.

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 gives concrete usage direction: choose 'backend_kind' or omit to auto-select, and put backend-specific options in 'connect_kwargs'. It doesn't explicitly mention when not to use it, but the action is contextual and obvious relative to siblings.

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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