Awesome/awesome-mcp-servers
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Merge pull request #1382 from mattjoyce/add-mattjoyce-servers

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Add mcp-construe and mcp-persona-sessions to Knowledge & Memory
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Frank Fiegel
2025-12-05 07:02:42 -06:00
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@@ -857,6 +857,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to
- [MWGMorningwood/Central-Memory-MCP](https://github.com/MWGMorningwood/Central-Memory-MCP) 📇 ☁️ - An Azure PaaS-hostable MCP server that provides a workspace-grounded knowledge graph for multiple developers using Azure Functions MCP triggers and Table storage.
- [pi22by7/In-Memoria](https://github.com/pi22by7/In-Memoria) 📇 🦀 🏠 🍎 🐧 🪟 - Persistent intelligence infrastructure for agentic development that gives AI coding assistants cumulative memory and pattern learning. Hybrid TypeScript/Rust implementation with local-first storage using SQLite + SurrealDB for semantic analysis and incremental codebase understanding.
- [pinecone-io/assistant-mcp](https://github.com/pinecone-io/assistant-mcp) 🎖️ 🦀 ☁️ - Connects to your Pinecone Assistant and gives the agent context from its knowledge engine.
- [mattjoyce/mcp-persona-sessions](https://github.com/mattjoyce/mcp-persona-sessions) 🐍 🏠 - Enable AI assistants to conduct structured, persona-driven sessions including interview preparation, personal reflection, and coaching conversations. Built-in timer management and performance evaluation tools.
- [ragieai/mcp-server](https://github.com/ragieai/ragie-mcp-server) 📇 ☁️ - Retrieve context from your [Ragie](https://www.ragie.ai) (RAG) knowledge base connected to integrations like Google Drive, Notion, JIRA and more.
- [redleaves/context-keeper](https://github.com/redleaves/context-keeper) 🏎️ 🏠 ☁️ 🍎 🪟 🐧 - LLM-driven context and memory management with wide-recall + precise-reranking RAG architecture. Features multi-dimensional retrieval (vector/timeline/knowledge graph), short/long-term memory, and complete MCP support (HTTP/WebSocket/SSE).
- [shinpr/mcp-local-rag](https://github.com/shinpr/mcp-local-rag) 📇 🏠 - Privacy-first document search server running entirely locally. Supports semantic search over PDFs, DOCX, TXT, and Markdown files with LanceDB vector storage and local embeddings - no API keys or cloud services required.