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Glama

search_assets

Find ready-made 3D assets from Poly Haven, Sketchfab, and Polypizza. Retrieve import IDs, license details, and real-world sizes to add models, textures, or HDRIs to Blender.

Instructions

Search ready-made assets. Results carry an id for import_asset, plus licence/author, real-world size (Poly Haven) and face counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryNo
sourceYespolyhaven: free CC0 HDRIs, PBR textures and realistic models (no key). sketchfab: huge catalogue, licences vary (search free; download needs a token). polypizza: stylised low-poly (free key).
licenceNopolypizza: 'CC0' or 'CC-BY'.
animatedNo
categoryNopolyhaven category ('furniture', 'wood'...), sketchfab category slug, or polypizza category ('Furniture & Decor', 'Nature', 'Animals').
previewsNoAttach thumbnails of the first N results (max 6) - cheaper than importing the wrong asset.
max_facesNosketchfab: only models up to this many faces.
asset_typeNopolyhaven only.all
min_size_mNopolyhaven: minimum real-world size (use 2+ for floors/walls so textures don't visibly repeat).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses what search results carry (id, licence/author, real-world size, face counts), which is real behavioral context. However it omits safety profile, rate limits, token/auth requirements for sketchfab, and pagination, leaving meaningful gaps for a 10-param, 3-source tool.

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?

Two tight sentences, front-loaded with the core purpose. Nothing is wasted, though the parenthetical '(Poly Haven)' is slightly cryptic without the schema context. Efficient overall.

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?

For a 10-parameter, multi-source tool with no output schema and no annotations, the description is thin. It partially compensates for the missing output schema by naming returned fields, but says nothing about pagination semantics, source-specific prerequisites, or how differing source behaviours affect results.

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 70%, so the schema already documents most parameters (source, licence, category, previews, max_faces, etc.). The description adds minor mapping via 'real-world size (Poly Haven)' and 'face counts', but does not clarify limit/pagination or source-specific parameter interactions beyond what the schema states.

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 (Search) and resource (ready-made assets), and immediately routes the output to the sibling import_asset by noting results carry an id for it. An agent can distinguish this discovery tool from import/creation 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?

Usage is only implied: the mention of 'an id for import_asset' hints at a search-then-import workflow, but there is no explicit when-to-use, when-not, or comparison against alternatives. No guidance on when to prefer this over directly calling import_model_file.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.