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Add LEGO Oracle (LEGO sets, parts, minifigs MCP server)
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@@ -1243,6 +1243,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to
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- [GistPad-MCP](https://github.com/lostintangent/gistpad-mcp) 📇 🏠 - Use GitHub Gists to manage and access your personal knowledge, daily notes, and reusable prompts. This acts as a companion to https://gistpad.dev and the [GistPad VS Code extension](https://aka.ms/gistpad).
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- [GetCacheOverflow/CacheOverflow](https://github.com/GetCacheOverflow/CacheOverflow) 📇 ☁️ - AI agent knowledge marketplace where agents share solutions and earn tokens. Search, publish, and unlock previously solved problems to reduce token usage and computational costs.
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- [graphlit-mcp-server](https://github.com/graphlit/graphlit-mcp-server) 📇 ☁️ - Ingest anything from Slack, Discord, websites, Google Drive, Linear or GitHub into a Graphlit project - and then search and retrieve relevant knowledge within an MCP client like Cursor, Windsurf or Cline.
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- [gregario/lego-oracle](https://github.com/gregario/lego-oracle) [](https://glama.ai/mcp/servers/gregario/lego-oracle) 📇 🏠 - LEGO sets, parts, minifigs, colours, and inventories from Rebrickable. 10 tools for searching sets, finding parts by colour, browsing themes, comparing sets, and discovering MOC alternate builds. 26k sets, 62k parts, 17k minifigs embedded.
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- [hannesrudolph/mcp-ragdocs](https://github.com/hannesrudolph/mcp-ragdocs) 🐍 🏠 - An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context
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- [harrison/ai-counsel](https://github.com/harrison/ai-counsel) - 🐍 🏠 🍎 🪟 🐧 - Deliberative consensus engine enabling multi-round debate between AI models with structured voting, convergence detection, and persistent decision graph memory.
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- [HatmanStack/ragstack-mcp](https://github.com/HatmanStack/RAGStack-Lambda/tree/main/src/ragstack-mcp) 🐍 ☁️ 🍎 🪟 🐧 - MCP server for RAGStack serverless knowledge bases. Search, chat with AI-generated answers, upload documents/media, scrape websites, and analyze metadata through an AWS-powered RAG pipeline (Lambda, Bedrock, S3, DynamoDB).
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