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manage_object_repo

Save, load, search, and list Blender objects in a local repository for reuse across projects.

Instructions

Object repository management: save, load, search, list_objects.

  • save: export Blender object to ~/.blender-mcp/repository/ with metadata

  • load: append saved object into current scene with optional transforms

  • search: filter index by query, category, tags, quality

  • list_objects: return full index

Repository location: ~/.blender-mcp/repository/

Return Format

Standard dict with keys: success, message, objects

Examples

await call_tool("manage_object_repo", {"operation": "list_objects"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
limitNo
queryNo
scaleNo
authorNo
publicNo
versionNo
categoryNogeneral
positionNo
object_idNo
operationNolist_objects
complexityNo
descriptionNo
min_qualityNo
object_nameNo
target_nameNo
quality_ratingNo
object_name_displayNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With all annotations false, the description carries full responsibility for disclosing side effects. It states that save exports to disk and load appends to the current scene, and it provides the return format. It does not mention overwrite behavior or selection requirements, but the main behavioral traits are covered.

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 well-structured: it opens with the purpose, uses a bullet list for operations, includes the repository location, return format, and a concrete example. Every sentence contributes value with no filler.

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

Completeness2/5

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

For a tool with 18 optional parameters and four distinct operations, this description is under-specified. It lacks per-operation parameter explanations, examples for save/load/search, and details about input requirements. The return format in the description mitigates only a small part of the completeness gap.

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?

The schema has 18 parameters with 0% description coverage, so the description must compensate. It only mentions 'operation', 'query', 'category', 'tags', 'quality', and 'optional transforms' indirectly. Most parameters (e.g., author, public, version, complexity, target_name) remain unexplained, leaving a significant 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?

The description states a specific verb ('manage') on a resource ('object repository') and then enumerates four concrete operations: save, load, search, list_objects. It also names the repository path, making it distinct from sibling asset or addon management tools.

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 through the operation list and repository path, but it does not explicitly state when to prefer this tool over alternatives like manage_asset_library, nor does it mention prerequisites or exclusions. Usage context is clear but not fully explicit.

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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