"A search about the concept and practice of magic" matching MCP servers:

Enables LLMs to perform conceptual search over local PDF/EPUB documents using a RAG pipeline with corpus-driven concept extraction and WordNet enrichment.

Enables access to a local Markdown knowledge base via MCP, currently only providing a ping tool.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

An MCP server that manages your conversation context automatically, pruning dead branches and keeping an audit trail of every cut.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

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

Automatically extracts technical concepts from AI coding conversations, organizes them into a searchable knowledge base with hierarchy and categories, and links them to specific locations in your codebase.

Enables local, read-only keyword search across past Claude Code, Codex, Gemini CLI, and ChatGPT conversations plus markdown notes, with exact source citations and context retrieval.

A scientific reasoning framework that leverages graph structures and the Model Context Protocol (MCP) to process complex scientific queries through an Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) approach.

Enables semantic search across conversation archives via MCP, allowing AI clients to retrieve relevant past conversations using vector embeddings and text fallback.

A federation MCP server that sits in front of multiple memory backends and presents a unified search surface to AI agents, allowing a single query to search across knowledge graphs, session history, and web search.

Full-text search over Claude Code conversation history using SQLite FTS5, exposing indexed transcripts as MCP tools for searching, browsing, and reading turns.

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.

An MCP server for managing contextual data as markdown files with metadata, enabling agents to save, retrieve, search, and delete contexts using simple CRUD operations.

Enables keyword search, filtering, and retrieval of Claude Code memory files, reducing context overhead by fetching only relevant memories on demand.

Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.

MCP server for Yandex Wiki with full-text search. Read and write pages, comments, attachments, and dynamic tables (grids); optional server-side read-only mode for agents. Docker-ready.

MCP bridge for PDF Content Search — full-text PDF search with Apple Vision OCR across thousands of documents in under a second from Claude, Cursor, or any MCP client. Advanced filters (date, category, sender, amount), wildcards, boolean operators. Bridge open-source (MIT), PDF Content Search app is commercial with free iOS+Android companion scanner apps.

AoT MCP server enables AI models to solve complex reasoning problems by decomposing them into independent, reusable atomic units of thought, featuring a powerful decomposition-contraction mechanism that allows for deep exploration of problem spaces while maintaining high confidence in conclusions.

Enables AI agents to search through Memex conversation history and local project files to retrieve specific commands, code snippets, and technology overviews. It utilizes smart context management and faceted filtering to provide relevant search results without causing context overload.

Persistent memory system for AI agents that records episodic memories with care-weighting and emotional valence, and provides full-text search with temporal chaining and automatic consolidation.

Enables fully local semantic search over a Roam Research graph via MCP, providing tools for hybrid retrieval, index refresh, and stats while keeping all content on-machine.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Enables AI agents to search and modify a personal knowledge base through OAuth-secured MCP tools, covering Obsidian vaults and Google Drive mirrors with semantic search, note read/write operations, audit logging, and eval-gated local classification.

Enables MCP clients to run offline-first web search, fetch and extract web content, build a local hybrid-retrieval knowledge base, and use persistent session memory without external API keys. It also supports intent-aware federated search, semantic reranking, entity graph exploration, and source-cited deep-search answers using local models.

A zero-config personal knowledge management MCP server that enables Claude Desktop to automatically classify, search, and organize local Markdown notes using hybrid search and Graph RAG.

Enables sophisticated reasoning workflows using graph-based representations for AI models.

Enables agents to run cross-runtime, cross-machine BM25 full-text search over historical conversations from Hermes, Google Antigravity, OpenClaw, Codex, and Claude Code, and to retrieve any past session's full transcript by ID. Queries return only ranked matching snippets, so recall costs no extra tokens or embeddings.

Enables Claude to store and retrieve information with semantic search using Qdrant vector database, providing persistent memory for conversations, code, and documentation.

Enables semantic search over local notes and documents using natural language queries. Supports multiple file types (Markdown, Python, HTML, JSON, CSV, text) with fast local embeddings and persistent ChromaDB vector storage.