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Tanshaydar/Quartermaster

by Various

Your game-asset vault - Unity Asset Store, Fab, Quixel Megascans, Gumroad and Leartes Cosmos - searchable by AI agents over MCP. Local, offline, no telemetry.

MCP

Tanshaydar/Quartermaster

Added 16 Sept 2026

#ai-agents #asset-management #assets #claude-desktop #cursor #fab #game-development #gumroad

Overview

Quartermaster is a local vault for game assets purchased from Unity Asset Store, Fab, Quixel Megascans, Gumroad, and Leartes Cosmos. It indexes these assets and makes them searchable by AI agents through Model Context Protocol (MCP). Runs locally, offline, with no telemetry.

Best for

Best for
Game developers who want a private, AI-searchable index of their purchased assets

Use cases

  • Index assets from multiple game marketplaces into one local catalog
  • Search a personal asset library via an AI assistant over MCP
  • Maintain a private, offline inventory of purchased game assets

How to use

Install

pip install -r requirements.txt

Tools exposed

  • server_port
  • embedding_model
  • fab_vault_dirs
  • media_cache_enabled

Tested with

Claude Desktop, Cursor, Windsurf

Notes

Quartermaster is a local vault for game assets purchased from Unity Asset Store, Fab, Quixel Megascans, Gumroad, and Leartes Cosmos. It indexes these assets and makes them searchable by AI agents through Model Context Protocol (MCP). Runs locally, offline, with no telemetry.

2 stars on GitHub. Last updated 2026-09-08. Licensed MIT.

Use cases

  • Index assets from multiple game marketplaces into one local catalog
  • Search a personal asset library via an AI assistant over MCP
  • Maintain a private, offline inventory of purchased game assets

Pros

  • Local and offline, with no cloud dependency
  • No telemetry, privacy-friendly
  • Exposes asset search to AI agents via a standard MCP interface

Cons

  • 2 GitHub stars indicates early-stage or niche adoption
  • Only covers the five listed marketplaces
  • Requires Python setup and an MCP client to use

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Local and offline, with no cloud dependency
  • No telemetry, privacy-friendly
  • Exposes asset search to AI agents via a standard MCP interface

Cons

  • 2 GitHub stars indicates early-stage or niche adoption
  • Only covers the five listed marketplaces
  • Requires Python setup and an MCP client to use

Pairs with

Other entries in the index that connect to this one. Click through to see the chain.

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