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glb_optimizer_convert

Convert FBX, OBJ (zip with .mtl and textures), glTF (zip), STL, PLY, DAE or 3DS to one GLB: Y-up, in meters, materials and FBX animations kept. Returns the GLB, a report, a validator pass and a preview render. Price: $0.15 USDC via x402, charged only on success. Part of GLB optimizer: Make a GLB small and fast for the web or mobile: dedup, instance, join, weld, simplify, resize and re-encode textures, meshopt or draco compression.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileNoModel file as base64: FBX, GLB, STL, PLY, DAE, 3DS, or a ZIP holding an OBJ/glTF with its .mtl/.bin and textures.
upAxisNoauto trusts FBX/glTF/DAE metadata, assumes Y-up for OBJ and Z-up for STL/PLY/3DS.auto
fileUrlNo
fileNameNoOriginal file name, helps tell the format apart.
unitScaleNoMultiply sizes by this to get meters; auto reads FBX units and treats large STLs as millimeters.auto

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the pricing model ($0.15 USDC via x402, charged only on success), the returned artifacts (GLB, report, validator pass, preview render), and preservation guarantees (materials and FBX animations kept). It stops short of stating auth flow or size/rate limits for the x402 payment.

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 core verb and format list are front-loaded, and the return/pricing details follow logically. The trailing 'Part of GLB optimizer: Make a GLB small and fast...' family blurb is promotional and only partially earns its space, slightly diluting an otherwise tight paragraph.

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 5-param, 0-required tool with no output schema, the description covers inputs, outputs, preservation guarantees and cost. The only gap is the ambiguity between the stated 'Y-up' output and the upAxis enum, which the description does not reconcile.

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?

Schema description coverage is 80%, so the schema already explains file, upAxis, unitScale and fileName. The description's format list and Y-up/meters statement echo the schema rather than adding new semantics (e.g., how axis conversion interacts with the upAxis enum values), so it stays at the baseline.

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?

States a specific verb (Convert) and enumerates the source resources (FBX, OBJ-zip, glTF-zip, STL, PLY, DAE, 3DS) into one GLB, with the output normalization (Y-up, meters) and preservation rules spelled out. An agent can distinguish this from the optimize/inspect/lod siblings without opening a schema.

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

Usage Guidelines3/5

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

The description implies usage (you need a non-GLB asset turned into GLB) but never states when to pick this over glb_optimizer_optimize or glb_optimizer_lod, nor any prerequisites beyond payment. Usage is inferable from context but not directed.

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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