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haotranq1234

Blockbench MCP Bridge

by haotranq1234

blockbench_build_from_reference

Creates a Blockbench model from an image reference using a structured blueprint that defines palette, bones, geometry, and animations. Supports dry run for validation.

Instructions

Build a Blockbench model from an image reference already inspected by ChatGPT/another vision-capable MCP client. The caller must translate the attached image into the structured blueprint: palette, semantic bone hierarchy, dimensions, symmetry, compressed geometry primitives, sockets, and animation tracks. Supports boxes, tapered stacks, alternating chains, ragged cloth, crystal clusters, skulls, rib cages, and trimmed armor plates. Use dry_run=true first for complex references; this validates and reports compiled cube counts without changing Blockbench. After build, always capture a four-view turntable and patch discrepancies against the original image.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNoCompile and validate only. Never changes Blockbench when true.
blueprintYes
Behavior3/5

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

With no annotations, the description must fully convey behavioral traits. It discloses that dry_run validates without side effects, and that the caller must translate images (no vision capability). However, it omits whether the tool creates or overwrites projects, error handling, authentication needs, or rate limits. The description adds some context but leaves gaps.

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 a single paragraph that efficiently packs the purpose, prerequisites, usage pattern, and supported geometry types. It is front-loaded with the core action. While it could benefit from bullet points for readability, it contains no fluff and every sentence adds value.

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?

Given the high complexity (nested blueprint, 8 geometry kinds, animations), the description provides a good overview but lacks details on return values (no output schema), success/failure indicators, and post-build state. The instruction to capture turntable and patch discrepancies implies the result is visual, but completeness is moderate.

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 50%, and the description adds high-level context by listing blueprint components (palette, bone hierarchy, geometry primitives, etc.). However, it does not detail individual nested properties or provide examples. The description complements the schema but does not fully compensate for the missing parameter descriptions.

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?

The description clearly states the tool builds a Blockbench model from an image reference, specifying that the caller must translate the image into a structured blueprint. It lists supported geometry primitives and distinguishes from sibling tools like blockbench_create_rig or blockbench_create_pet by focusing on image-driven reconstruction.

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

Usage Guidelines4/5

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

The description provides actionable guidelines: use dry_run=true first for complex references, and after a successful build, capture a four-view turntable and patch discrepancies. It implicitly warns that the tool does not process images itself, requiring the caller to provide the blueprint. However, it does not explicitly state when not to use this tool or compare with alternatives.

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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