fix: use ontorata/ratary owner/repo format and alphabetical placement

Co-authored-by: Cursor <cursoragent@cursor.com>

README.md

@@ -2170,6 +2170,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to
 - [novyxlabs/novyx-mcp](https://github.com/novyxlabs/novyx-core/tree/main/packages/novyx-mcp) [![novyx-mcp-desktop MCP server](https://glama.ai/mcp/servers/@novyxlabs/novyx-mcp-desktop/badges/score.svg)](https://glama.ai/mcp/servers/@novyxlabs/novyx-mcp-desktop) 🐍 🏠 ☁️ 🍎 🪟 🐧 - Persistent AI agent memory with rollback, audit trails, semantic search, and knowledge graph. Zero-config local SQLite mode or cloud API. 23 tools, 6 resources, 3 prompts.
 - [olgasafonova/mediawiki-mcp-server](https://github.com/olgasafonova/mediawiki-mcp-server) 🏎️ ☁️ 🏠 🍎 🪟 🐧 - Connect to any MediaWiki wiki (Wikipedia, Fandom, corporate wikis). 33+ tools for search, read, edit, link analysis, revision history, and Markdown conversion. Supports stdio and HTTP transport.
 - [omega-memory/omega-memory](https://github.com/omega-memory/omega-memory) 🐍 🏠 🍎 🪟 🐧 - Persistent memory for AI coding agents with semantic search, auto-capture, cross-session learning, and intelligent forgetting. 28 MCP tools, local-first.
+- [ontorata/ratary](https://github.com/ontorata/ratary) [![ontorata/ratary MCP server](https://glama.ai/mcp/servers/ontorata/ratary/badges/score.svg)](https://glama.ai/mcp/servers/ontorata/ratary) 📇 🏠 ☁️ 🍎 🪟 🐧 - Persistent coding memory for AI assistants — hybrid search, knowledge graph, token-efficient context. MCP stdio (28 tools), npm `@ratary/mcp-server`, optional remote Streamable HTTP. Self-host D1, Postgres, MariaDB, or Docker.
 - [oomkapwn/enquire-mcp](https://github.com/oomkapwn/enquire-mcp) [![oomkapwn/enquire-mcp MCP server](https://glama.ai/mcp/servers/oomkapwn/enquire-mcp/badges/score.svg)](https://glama.ai/mcp/servers/oomkapwn/enquire-mcp) 📇 🏠 🍎 🪟 🐧 - Long-term memory for AI agents (Claude Code/Desktop, Cursor, ChatGPT, Codex, OpenClaw) backed by a local Obsidian markdown vault. Hybrid retrieval (BM25 + ML embeddings + BGE reranker, RRF-fused), HNSW + int8 quantization, agentic RAG (HyDE + sub-question), GraphRAG-light (Louvain wikilink community detection), standalone Obsidian Bases, PDFs + Tesseract OCR. 46 tools, 19 MCP prompts, MIT, SLSA L2, zero cloud calls during serve. `npx -y @oomkapwn/enquire-mcp serve --vault <path>`
 - [AgentBase1/mcp-server](https://github.com/AgentBase1/mcp-server) [![mcp-server MCP server](https://glama.ai/mcp/servers/AgentBase1/mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/AgentBase1/mcp-server) 📇 ☁️ - AgentBase: open registry of agent instruction files for AI agents. Search and retrieve system prompts, skills, workflows, domain packs, and safety filters via MCP tools. 44 files, CC0-licensed, free.
 - [pallaprolus/mendeley-mcp](https://github.com/pallaprolus/mendeley-mcp) 🐍 ☁️ - MCP server for Mendeley reference manager. Search your library, browse folders, get document metadata, search the global catalog, and add papers to your collection.
@@ -2187,7 +2188,6 @@ Persistent memory storage using knowledge graph structures. Enables AI models to
 - [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.
 - [pomazanbohdan/memory-mcp-1file](https://github.com/pomazanbohdan/memory-mcp-1file) 🦀 🏠 🍎 🪟 🐧 - A self-contained Memory server with single-binary architecture (embedded DB & models, no dependencies). Provides persistent semantic and graph-based memory for AI agents.
 - [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.
-- [Ratary](https://github.com/ontorata/ratary/tree/main/MCP) [![Ratary MCP server](https://glama.ai/mcp/servers/ontorata/ratary/badges/score.svg)](https://glama.ai/mcp/servers/ontorata/ratary) 📇 🏠 ☁️ 🍎 🪟 🐧 - Persistent coding memory for AI assistants — hybrid search, knowledge graph, token-efficient context. MCP stdio (28 tools), npm `@ratary/mcp-server`, optional remote Streamable HTTP. Self-host D1, Postgres, MariaDB, or Docker.
 - [ravi-labs/mindmap-mcp-server](https://github.com/ravi-labs/mindmap-mcp-server) [![ravi-labs/mindmap-mcp-server MCP server](https://glama.ai/mcp/servers/ravi-labs/mindmap-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/ravi-labs/mindmap-mcp-server) 📇 🏠 - Local-first memory & context-handoff across AI tools — capture context in one tool, resume it in another, with graceful decay, a persona layer, and a portable memory passport.
 - [remembra-ai/remembra](https://github.com/remembra-ai/remembra) [![remembra MCP server](https://glama.ai/mcp/servers/remembra-ai/remembra/badges/score.svg)](https://glama.ai/mcp/servers/remembra-ai/remembra) 🐍 📇 🏠 ☁️ 🍎 🪟 🐧 - Persistent memory layer for AI agents with entity resolution, PII detection, AES-256-GCM encryption at rest, and hybrid search. 100% on LoCoMo benchmark. Self-hosted.
 - [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).