Add A2CR MCP server

akagi819 committed May 16, 2026 at 07:20 UTC b8f24f01767a4b53b77ea53d576e1a2b6a30fba2
1 file changed +1
README.md
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@@ -1561,6 +1561,7 @@ Control smart home devices, home network equipment, and automation systems.
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1562 Persistent memory storage using knowledge graph structures. Enables AI models to maintain and query structured information across sessions.
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1564 +- [a2cr/a2cr](https://github.com/a2cr/a2cr) 🐍 ☁️ 🏠 🍎 🪟 🐧 - MCP server for AI-agent handoffs. Saves client-encrypted WorkBaton checkpoints and WorkStash notes so Codex, Claude Code, Roo Code, and other MCP clients can resume work without passing full chat history.
1565 - [aidesignblueprint/integrations](https://github.com/aidesignblueprint/integrations) [![aidesignblueprint/integrations MCP server](https://glama.ai/mcp/servers/aidesignblueprint/integrations/badges/score.svg)](https://glama.ai/mcp/servers/aidesignblueprint/integrations) 🐍 ☁️ - Read-only doctrine access for the Agentic AI Blueprint — the industry standard reference for safe, observable, and steerable AI agent UX. Browse and search 10 principles, clusters, curated implementation examples, and application guides for production agentic AI systems. 13 public tools require no credentials. `https://aidesignblueprint.com/mcp`
1566 - [andreas-roennestad/openhive-mcp](https://github.com/andreas-roennestad/openhive-mcp) [![openhive-mcp MCP server](https://glama.ai/mcp/servers/andreas-roennestad/openhive-mcp/badges/score.svg)](https://glama.ai/mcp/servers/andreas-roennestad/openhive-mcp) 📇 ☁️ - Shared knowledge base where AI agents search and post problem-solution pairs. Agents query before solving, post after resolving. Semantic search via pgvector, duplicate detection, auto-scoring. `npx openhive-mcp`, Free to use
1567 - [Auctalis/nocturnusai](https://github.com/Auctalis/nocturnusai) [![Auctalis/nocturnusai MCP server](https://glama.ai/mcp/servers/Auctalis/nocturnusai/badges/score.svg)](https://glama.ai/mcp/servers/Auctalis/nocturnusai) 🐍 🏠 - Deterministic reasoning engine for AI agent context compression. Extracts structured facts with logical inference, proof chains, and truth maintenance. REST API, Python/TypeScript SDKs, and MCP server integration.