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gnshb

salome-mcp

by gnshb

execute_salome_code

Run arbitrary Python code inside SALOME to automate tasks and return a result variable. Use for custom CAD modeling and meshing beyond standard operations.

Instructions

Execute arbitrary Python code in the SALOME process.

The executed code can set a result variable that will be returned.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It mentions that code is executed and a `result` variable can be returned, but it does not warn about potential side effects, state changes, or the destructive capability of arbitrary code in a SALOME process. This is a significant transparency gap for such a powerful tool.

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 two sentences long, front-loads the core purpose, and contains no unnecessary words. Every sentence adds value, making it highly concise and well-structured.

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

Completeness2/5

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

Despite having an output schema, the description does not provide essential context for a code execution tool: it omits that the code runs in the SALOME Python interpreter, can mutate the study, and should be used for custom operations. The minimal detail leaves the tool underspecified given its power and complexity.

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?

The schema has one undocumented parameter (code), so the description partially compensates by explaining that the code can set a `result` variable to be returned. This gives the parameter meaning, but it lacks examples, environment details, or error handling expectations, leaving room for improvement.

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 a specific verb and resource: 'Execute arbitrary Python code in the SALOME process.' It distinguishes itself from sibling tools that perform specific operations by emphasizing arbitrary code execution, making the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this tool versus its siblings. It does not mention that it serves as a fallback for operations not covered by dedicated tools, nor does it provide any contextual cues for appropriate usage beyond the generic description.

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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