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Glama

check_model

Detect floating (unconnected to ground) and loose (not attached to main body) groups in LEGO models to ensure structural integrity.

Instructions

Check structure: floating groups (not connected to anything touching the ground) and loose groups (standing on the ground but not attached to the main body).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. 'Check' strongly implies a read-only analysis, and the output schema covers return values, but the description does not explicitly state side effects, permissions, or that the model is not modified. It does add useful domain definitions of floating and loose groups.

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 a single front-loaded sentence, with parenthetical definitions that earn their place by clarifying the two structural failure modes. There is no redundant or wasted text.

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?

For a no-argument check tool with an output schema, the description explains what structural conditions are detected, which is the core semantic need. It omits usage timing and whether it targets the currently loaded model, but the output schema covers return details and the tool is otherwise simple.

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 tool has zero parameters, so there are no parameter semantics to document. Per the baseline rule for a 0-parameter tool, a 4 is appropriate because the schema cannot be enriched further from the description.

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 gives a specific verb ('Check') and resource ('structure'), then defines the two diagnostics: floating groups and loose groups. It is clearly not a generic model operation, though it does not explicitly differentiate itself from siblings like describe_model or render_model.

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?

No when-to-use guidance, prerequisites, or alternatives are provided. The description only defines the categories it checks; it never states when an agent should call this tool versus inspecting the model another way.

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