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mohakmalviya

blender-compact-mcp

by mohakmalviya
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Python
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execute

Run up to 100 batched Blender operations in one call, returning counts and errors instead of full scene data. Use dry-run to validate syntax and permissions before applying changes.

Instructions

Execute up to 100 {'op': name, ...arguments} steps. Returns counts/errors, not full scene data. dry_run checks syntax/permissions only. Runtime failures are partial, not transactional.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stepsYes
dry_runNo
timeoutNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv0.2.1
    • removedInput schema / properties / dry_run / title
      Removed value: -"Dry Run"
    • removedInput schema / properties / steps / items / additionalProperties
      Removed value: -true
    • removedInput schema / properties / steps / title
      Removed value: -"Steps"
    • removedInput schema / properties / timeout / title
      Removed value: -"Timeout"
    • removedInput schema / title
      Removed value: -"executeArguments"
  2. First observedv0.2.0

TDQS

B3.3/5.0
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 return shape ('counts/errors, not full scene data'), that dry_run is non-mutating ('syntax/permissions only'), and that runtime failures are 'partial, not transactional'. This is strong negative-consequence disclosure; only auth/permission requirements are left unstated.

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?

Four tight sentences, each carrying distinct information, with the batching limit and return semantics front-loaded. No filler.

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 batch/partial-failure mutation tool with an output schema, the description covers batching limits, return scope, dry-run behavior, and non-transactional failure. The unmentioned timeout param is the only notable omission.

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 coverage is 0%, so the description must compensate. It meaningfully explains two of three params: steps as up to 100 op objects and dry_run as syntax/permission-only validation. The timeout parameter is never mentioned, leaving a real gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Execute) and the resource shape ('{'op': name, ...arguments} steps'), and implies the domain via 'not full scene data'. However, it never says what an 'op' is or what system it operates on, and gives no differentiation from siblings inspect/discover/capture.

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 guidance on when to use execute versus the sibling tools (inspect, discover, capture), and no prerequisites or exclusions stated. The dry_run note implies a validation use case but does not frame it as a when-to-use recommendation.

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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