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Glama

create_mesh

Construct a mesh from vertices, edges, and faces. Indices are validated first, so bad face references return a clean error rather than a broken object.

Instructions

Build a mesh from explicit geometry.

Indices are validated before anything is created, so a bad face index is a clean error rather than a broken mesh. Keep it to a few thousand vertices; for anything procedural and large, use execute_blender_python with bmesh.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName for the new object
edgesNo[[i, j], ...] for standalone edges; faces create their own edges
facesNo[[i, j, k, ...], ...] indices into vertices, counter-clockwise seen from outside
scaleNo[x, y, z] in Blender units
locationNo[x, y, z] in Blender units
rotationNo[x, y, z] euler rotation in DEGREES
verticesYes[[x, y, z], ...] in local space
collectionNo
shade_smoothNo
recalculate_normalsNoMake face normals consistent afterwards

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context: indices are validated before creation, resulting in a clean error rather than a broken mesh, and a practical scale limit. This goes beyond what the schema reveals and helps the agent anticipate failure modes.

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 short paragraphs, front-loaded with the core purpose, followed by two high-value behavioral notes. Every sentence earns its place; there is no filler or redundancy.

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?

Given the tool has 10 parameters and no output schema, the description covers the essential context: what it builds, when to avoid it, and how errors are handled. It doesn't explain the return value or whether it selects the new object, but the schema covers parameter semantics and the description fills the most important operational gaps.

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 most parameters (80% coverage), so the baseline is 3. The description adds meaning to indices and vertices by explaining that bad face indices produce clean errors and that the tool is suited for a few thousand vertices. This enriches the semantics of the faces/edges and vertices parameters beyond their type definitions.

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 the tool builds a mesh from explicit geometry, which sets it apart from parametric primitive creation tools like create_primitive or create_cube. It doesn't name siblings explicitly, but 'explicit geometry' is a meaningful distinction among the mesh-creation tools.

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

Usage Guidelines5/5

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

The description gives an explicit when-to-use and when-not-to-use: 'Keep it to a few thousand vertices; for anything procedural and large, use execute_blender_python with bmesh.' This directly names the alternative and the condition, making the decision unambiguous.

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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