A
license-
qualityC
maintenanceA Model Context Protocol server that enables semantic search and retrieval of Apple Notes content, allowing AI assistants to access, search, and create notes using on-device embeddings.
2,673
2
MIT
72,476 servers. Updated
Search Relevance ↓
Recent Usage ↓
Date Added ↓
Date Updated ↓
Weekly Downloads ↓
GitHub Stars ↓
Recent GitHub Stars ↓
Matching MCP tools:
Matching MCP Connectors:
io.github.pipeworx-io
ai.odei
com.csidia
com.splidia
"How to connect and write Zettelkasten notes" matching MCP servers:
- Alicense-qualityBmaintenanceMCP server that indexes your Obsidian notes into a Milvus vector database and enables querying them via a local or OpenAI LLM, with real-time synchronization.3MIT
- Flicense-qualityBmaintenanceEnables semantic search over personal markdown notes by indexing them into a vector database and exposing search, reindex, and status tools via MCP.
- Alicense-qualityAmaintenanceUniversal MCP server for readonly-first access to Oracle, SQL Server, PostgreSQL, MySQL/MariaDB, SQLite, MongoDB, and Qdrant vector search.7691MIT
- AlicenseDqualityCmaintenanceA lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.12MIT
- Flicense-qualityCmaintenanceEnables semantic search over personal study notes by exposing a vector search tool that Claude Desktop can call to retrieve relevant note content and synthesize grounded answers.
Tribalofficial
FlicenseAqualityAmaintenanceSemantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.109- AlicenseAqualityAmaintenanceA Byzantine fault-tolerant MCP Memory server that uses SQLite write-ahead logging and FAISS hybrid vector search for deterministic, secure memory operations.3Apache 2.0
- AlicenseAqualityDmaintenanceA Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.355Apache 2.0
- AlicenseAqualityDmaintenanceIndexes codebases using semantic embeddings for natural language search, enabling developers to find code with queries like 'how does authentication work'.81MIT
- FlicenseAqualityDmaintenanceA ChromaDB-based document storage MCP server that enables adding, storing, and summarizing notes with a custom note:// URI scheme and a single tool for note creation.3
mcp-server-qdrantofficial
AlicenseBqualityCmaintenanceThis repository is an example of how to create a MCP server for Qdrant, a vector search engine.21,499Apache 2.0- AlicenseBqualityBmaintenanceProvides persistent memory management for AI agents with 14 tools, including semantic search, context synthesis, auto-context middleware, and write-back actions to external services like Slack.18795MIT
- FlicenseBqualityDmaintenanceA Model Context Protocol server that enables semantic vector search over Obsidian notes, allowing AI assistants to intelligently search and retrieve relevant note content.12
- AlicenseBqualityCmaintenanceProvides a local-first secure memory store with encrypted payloads, entity extraction, and vector persistence, exposing read, write, and delete tools with granular capability controls.4Apache 2.0
- AlicenseBqualityDmaintenanceAn MCP server for providing semantically related Ethereum Improvement Proposals (EIPs) to AI agents.13MIT
@manasdb/mcp-serverofficial
Alicense-qualityDmaintenanceProvides a plug-and-play persistent memory layer for MCP-compatible AI assistants, enabling them to store, retrieve, and delete memories across multiple databases simultaneously using semantic vector search.15MIT
MCP MariaDB Serverofficial
Alicense-qualityCmaintenanceAn interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.189MIT- Alicense-qualityAmaintenanceMCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.MIT