Rengi Forge

glb_optimizer_inspect

Inspect a GLB without changing it: triangles, draw calls, materials, texture memory, animations, glTF validator errors, and concrete suggestions to make it lighter. Price: $0.05 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.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "glb_optimizer_inspect"
  }
}

Input Schema

Name	Required	Description	Default
`glb`	No
`glbUrl`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A3.7/5.0

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 disclose meaningful behavior: it is read-only, costs $0.05 USDC via x402, and is charged only on success. It stops short of explaining auth/wallet requirements or what happens on an invalid GLB, so it is strong but not complete.

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?

Purpose is front-loaded in the first clause, and the output list is compact. The pricing detail is relevant, but the trailing family blurb ('dedup, instance, join, weld, simplify...') reads partly as promotion and adds little to this specific tool's invocation.

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

Completeness3/5

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

The enumerated return fields partially compensate for the absent output schema, and pricing covers the missing annotations. However, the two undocumented, non-required input parameters leave the agent unsure how to actually supply the GLB, which is essential for calling the tool correctly.

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 coverage is 0% for two parameters (glb, glbUrl), and the description never mentions either. It does not say that one of the two must be supplied, which takes precedence if both are given, or what forms glb accepts (path vs base64). This is the definition's largest gap.

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?

Specific verb (inspect) plus resource (GLB) plus an explicit non-mutation scope ('without changing it'), and it enumerates the concrete outputs the agent will get. The 'Part of GLB optimizer' sentence positions it within the family, making it distinguishable from the optimize/convert/lod siblings.

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?

'Inspect a GLB without changing it' implies a diagnostic/pre-optimization role, but there is no explicit when-to-use statement, no prerequisite, and no named alternative such as glb_optimizer_optimize. Usage must be inferred from the read-only framing.

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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