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Frank Fiegel
2026-09-07 13:53:17 -06:00
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@@ -3216,7 +3216,7 @@ Tools for conducting research, surveys, interviews, and data collection.
- [andyliszewski/grounding-ai](https://github.com/andyliszewski/grounding-ai) [![andyliszewski/grounding-ai MCP server](https://glama.ai/mcp/servers/andyliszewski/grounding-ai/badges/score.svg)](https://glama.ai/mcp/servers/andyliszewski/grounding-ai) 🐍 🏠 - Build a searchable index from PDFs, EPUBs, and Word docs. Claude queries it via MCP and pulls grounded answers with exact page-and-section citations. Local-first, no cloud dependency.
- [gogabrielordonez/mcp-ragchat](https://github.com/gogabrielordonez/mcp-ragchat) 📇 🏠 - Add RAG-powered AI chat to any website with one command. Local vector store, multi-provider LLM (OpenAI/Anthropic/Gemini), self-contained chat server and embeddable widget.
- [notwhiteblank/scholar-rag-mcp](https://github.com/notwhiteblank/scholar-rag-mcp) 🐍 🏠 🐧 - Academic-paper knowledge base over local PDFs: MinerU parsing, metadata normalization, section annotation, Qdrant vector search with reranking, and context-safe paginated full-text reading. 11 tools including two-phase KB deletion; runs against local or OpenAI-compatible embedding/rerank/chat models (vLLM).
- [notwhiteblank/scholar-rag-mcp](https://github.com/notwhiteblank/scholar-rag-mcp) [![notwhiteblank/scholar-rag-mcp MCP server](https://glama.ai/mcp/servers/notwhiteblank/scholar-rag-mcp/badges/score.svg)](https://glama.ai/mcp/servers/notwhiteblank/scholar-rag-mcp) 🐍 🏠 🐧 - Academic-paper knowledge base over local PDFs: MinerU parsing, metadata normalization, section annotation, Qdrant vector search with reranking, and context-safe paginated full-text reading. 11 tools including two-phase KB deletion; runs against local or OpenAI-compatible embedding/rerank/chat models (vLLM).
- [poll-the-people/customgpt-mcp](https://github.com/Poll-The-People/customgpt-mcp) 🐍 🏠 ☁️ - An MCP server for accessing all of CustomGPT.ai's anti-hallucination RAG-as-a-service API endpoints.
- [vectara/vectara-mcp](https://github.com/vectara/vectara-mcp) 🐍 🏠 ☁️ - An MCP server for accessing Vectara's trusted RAG-as-a-service platform.
- [zaharajabeen13-create/ai-rete-rag-mcp](https://github.com/zaharajabeen13-create/ai-rete-rag-mcp) [![zaharajabeen13-create/ai-rete-rag-mcp MCP server](https://glama.ai/mcp/servers/zaharajabeen13-create/ai-rete-rag-mcp/badges/score.svg)](https://glama.ai/mcp/servers/zaharajabeen13-create/ai-rete-rag-mcp) 🐍 ☁️ - Decisions the model doesn't make: a Rete rule engine returns the verdict, retrieval over your own policy documents explains it — so an explanation can't invent a threshold your rulebook doesn't have. Turn a written policy into draft rules that cite the sentence each encodes, save them as YAML, then decide; every verdict names the rule that fired, and you can ask why any rule didn't.