"A comparison or overview of Obsidian and Logseq" matching MCP servers:

Enables fetching Google's AI Overview answers and cited sources as structured JSON, with support for batch queries and country/language targeting.

Provides MCP tools to search, retrieve, and answer questions from the public Open Finance Brasil Confluence documentation using BM25-based local indexing.

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

Provides search and retrieval over a local database of Pathfinder 2e rules. Enables Claude Code to answer rules questions with cited, accurate answers from fetched entries.

Enables semantic search and note management for Obsidian vaults via the Model Context Protocol, allowing LLMs to search, read, and index notes, PDFs, and web pages locally.

Provides semantic search over Obsidian markdown notes using BAAI/bge-m3 embeddings and ChromaDB, accessible via the Model Context Protocol.

Enables semantic search over local knowledge bases like Obsidian notes, SQLite, and plaintext files, exposing results to Claude via MCP server.

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.

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

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

Enables any MCP client to semantically search, traverse, and manage a local markdown note vault through a shared LanceDB-backed RAG index. It supports natural-language queries, backlink and wikilink traversal, link repair, daily journaling, and note creation, editing, and frontmatter updates.

Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.

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.

An MCP server that uses Groq's API to expose raw chain-of-thought tokens from Qwen's qwq model, enabling LLMs to think step-by-step before responding.

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

Enables semantic search over an Obsidian vault using natural language, retrieving relevant notes and extracted conclusions.

Provides ranked full-text search over Obsidian vaults using SQLite FTS5, BM25 scoring, and multilingual stemming, adding a vault_fts_search tool to find relevant notes beyond exact line matches.

Enables AI agents to search and retrieve historical Spanish Call of Duty gaming community posts, with free search previews and pay-per-page citation-ready access via x402.

Enables AI clients to search and retrieve notes from a local Markdown vault using hybrid keyword/semantic search, and to save typed memories with append and guarded replacement operations.

Enables hybrid vector-BM25 search and retrieval of Obsidian vault knowledge notes, allowing AI assistants like Claude to efficiently access relevant chunks without full file reads.

Provides local hybrid search over an Obsidian vault via the Model Context Protocol. It enables Claude Code to retrieve relevant notes with citations, using on-device embeddings and BM25 rank fusion.

Enables an AI assistant to search, read, navigate links, and write only to the Inbox of a local Obsidian vault. It provides hybrid retrieval with reranking, restricted writing, and built-in evals for retrieval, tool use, and prompt injection.

Enables indexing and searching Obsidian notes via MCP, using Cloudflare's serverless platform for cost-effective vector embeddings and AI-powered search.

Enables semantic search and content retrieval from local Obsidian vaults through the Model Context Protocol. It allows AI assistants to query notes by meaning, filter by tags, and access full note content for enhanced knowledge integration.

MCP server that indexes Obsidian notes and enables hybrid search (full-text, fuzzy, semantic) for AI assistants to find and read notes.

Transforms prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning quality while reducing token usage by up to 92.4%, supporting multiple LLM providers including Claude, GPT, Ollama, and local models.

Enables semantic search and retrieval over an Obsidian vault using local or API-based embeddings, allowing AI assistants to find notes by meaning, get related content, and pull context during conversations.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables natural-language and keyword search across Obsidian notes, returning reranked passages with provenance, and supports reading notes and incremental indexing.