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
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_portembedding_modelfab_vault_dirsmedia_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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