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Ansys CFX-MCP

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by ansys

connect

Establishes a connection to a selected backend, such as PyCFX, enabling subsequent CFX operations. Specify backend kind and connection options to configure.

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?

No annotations are provided, so the description carries the burden of disclosure. It explains that connect_kwargs are forwarded to the backend and mentions auto-selection behavior. However, it does not disclose potential side effects like disconnecting an existing connection or whether repeated connections are allowed, leaving some behavioral ambiguity.

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 and front-loaded: it states the purpose in the first sentence, then provides essential usage details in the second. No wasted words, and the structure is easy to scan.

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 description is fairly complete given the tool's complexity. It covers backend selection and argument forwarding, and the output schema handles return values. It could mention state implications, but overall it provides enough context for effective use.

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?

The schema already covers both parameters, but the description adds value by listing the valid backend kind ('pycfx') and giving examples of backend-specific options (url/token/ip/port). This goes beyond the schema's generic descriptions, making it easier for an agent to construct proper arguments.

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 function: 'Connect the cfx leaf to a backend.' It specifies the resource (backend) and the action (connect), and distinguishes itself from siblings like disconnect and session_status. It also lists available backend kinds, making the purpose unambiguous.

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 guidance on how to use the tool: pass backend_kind to choose a backend or omit to auto-select, and put backend-specific options in connect_kwargs. It does not explicitly mention when not to use it or alternative tools, but the context is clear enough for an agent to decide.

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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