"Information about obsidian" matching MCP servers:

MCP server that integrates with LM Studio to provide a search_notes tool, allowing the chat model to retrieve and answer from a local Obsidian vault.

Model-agnostic, agent-ready Obsidian MCP server with RBAC, SLSA provenance, and native search. 163 tools across 31 domains, multi-vault, pluggable embeddings.

Standalone MCP server for Obsidian vaults - hybrid search (FTS5 + vector + cross-encoder reranking), images and PDFs in agent-readable form, Kanban-aware tasks (Tasks-plugin + Dataview formats), structured memory with topic recall, fine-grained read/write tools for optimal token efficiency, and link graph support. Run locally, self-host, or one-click deploy for remote access. OAuth 2.1.

Enables AI assistants to search and ask questions about documents stored in AnythingLLM workspaces through MCP tools.

Enables AI assistants to search and retrieve transcripts from the Entra.Chat podcast about Microsoft Entra ID, with timestamped YouTube links and guest information.

Enables semantic search and reading of Obsidian Markdown notes through read-only MCP tools, allowing Claude to retrieve relevant passages from a local vault.

MCP server for semantic search in an Obsidian Second Brain vault using self-hosted Qdrant and Google Gemini embeddings.

Read-only semantic retrieval for agents that need to find the right Obsidian note without write access.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

A Model Context Protocol server that allows AI agents to perform WHOIS lookups, enabling users to directly ask the AI about domain availability, ownership, registration details, and other domain information.

Provides LLMs with specialized knowledge about fountain pen inks, enabling intelligent ink search, color matching, and recommendations through various tools.

A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

Turns local code repositories into a queryable dependency graph built from AST parsing and git co-edit history, then exposes it alongside an Obsidian vault as persistent memory. Enables context retrieval, impact analysis of proposed changes, and curation of durable prose knowledge through full-text search and Personalized PageRank classification—all fully offline without API keys or embeddings.

A server that connects MCP clients (Claude, Cursor, Windsurf, etc.) to EventCatalog instances, allowing users to ask questions about their event-driven architecture and get insights about domains, services, and message schemas directly within their development tools.

A Model Context Protocol server that provides natural language access to Cortex API, enabling users to query information about their system's structure and make faster decisions about services, incidents, and team responsibilities.

Provides a hybrid search engine for Obsidian vaults, enabling LLM agents to query notes with BM25 keyword and vector semantic search, metadata filtering, and sibling-document retrieval.

Exposes a personalized AI agent that reads your resume and provides intelligent responses about your professional background through a standardized MCP server interface with RAG capabilities.

A persistent memory layer for Claude Desktop that enables semantic search and CRUD operations on your Obsidian vault using local embeddings and PostgreSQL+pgvector.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.