salome-mcp

by gnshb

Overview Schema Source Related Servers Discussions Admin

Python

Local

create_mesh

Generate a mesh on a CAD geometry by specifying surface and volume algorithms, segment counts, and element size limits.

Instructions

Create a mesh definition on GEOM object using standard hypotheses.

surface_algorithm: triangle | quadrangle | none volume_algorithm: tetrahedron | hexahedron | none

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": {
      "shape_ref": "<shape_ref>"
    },
    "name": "create_mesh"
  }
}

Input Schema

Name	Required	Default
`mesh_name`	No	Mesh
`shape_ref`	Yes
`segment_count`	No
`max_element_area`	No
`volume_algorithm`	No	tetrahedron
`surface_algorithm`	No	triangle
`max_element_volume`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 1, 2026

TDQS

C2.7/5.0

Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It states the tool creates a mesh definition, but does not explain whether it modifies the GEOM object, whether it requires prerequisites, whether it replaces existing meshes, or what the side effects are. 'Standard hypotheses' is also vague.

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 very concise, with just two sentences. It front-loads the main purpose and includes a compact parameter list. However, the parameter list is presented as a simple enumeration without context, and the structure could be improved with clearer headings or formatting.

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 is incomplete for a tool with 7 parameters, especially considering sibling tools like create_mesh_with_hypotheses and compute_mesh. It does not clarify what 'standard hypotheses' means, how parameters interact, or what the resulting mesh definition entails.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It lists allowed values for surface_algorithm and volume_algorithm, but provides no explanation for the other five parameters (mesh_name, shape_ref, segment_count, max_element_area, max_element_volume). This partial coverage is insufficient for a 7-parameter tool.

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 a specific action ('Create a mesh definition on GEOM object') and identifies the resource (GEOM object). It distinguishes from 'compute_mesh' but does not differentiate from sibling 'create_mesh_with_hypotheses', so it lacks full sibling differentiation.

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 alternatives. The phrase 'using standard hypotheses' vaguely implies a contrast with a custom-hypotheses tool, but no concrete when-to-use or when-not-to-use instructions are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.