"Using personal browser with existing cookies and session data" matching MCP servers:

Enables browsing, searching, and importing sessions from multiple coding agents into the current one, or reopening them in their native agent, all locally and read-only.

A personal AI memory service based on MCP protocol that provides long-term memory capabilities for various AI clients.

Enables persistent session memory for LLMs by trust-scoring and injecting verified context between conversations.

Enables querying, listing, and summarizing personal knowledge base documents using RAG with hybrid search and LLM.

Enables MCP-compatible AI clients to securely access a user's personal context by exposing connector data, starting with recent Spotify listening activity, through per-user OAuth and tools.

A privacy-first memory layer for AI that securely bridges local knowledge with AI assistants through MCP tools, with intelligent redaction and local vector database.

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.

Provides AI assistants with persistent, graph-based memory using Neo4j, enabling semantic search and complex relationship tracking. It features specialized Cloudflare Access support for secure remote connections and isolated multi-database management for different projects.

Enables LibreChat agents to manage per-user private files, persistent documentation, RAG indexing and semantic search, and publish files via secure opaque public links.

Syncs AI conversations from multiple platforms to local Markdown files for your second brain, and provides browser automation tools via MCP for agentic workflows.

MCP server providing weather lookup, semantic search over personal notes (RAG), and image generation tools for any MCP client like Claude Desktop.

Provides AI-assisted long-term memory storage and retrieval with exact-phrase and semantic hybrid search, enabling save, read, delete, and admin recovery via MCP tools.

Indexes local and enterprise documents to provide a unified personal knowledge base for AI clients via the Model Context Protocol. It supports full-text search across various file formats and integrates with platforms like Feishu and WeChat Work.

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

MCP 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.

Enables AI agents to query structured data refined from unstructured web sources, including developer breaking changes, B2B pricing matrices, regulatory compliance, semantic search, and on-demand URL refinement.

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.

Enables semantic search over a personal document corpus through MCP tools, allowing users to retrieve ranked chunks, access full document context, and list sources from their semester notes.

A Personal Knowledge-Base MCP Server that provides semantic search over a student-owned document collection using MCP, Gemini embeddings, and Qdrant. It exposes tools for searching notes, retrieving full documents, and listing indexed sources.

Personal local AI memory/document MCP that manages short and long-term memories and Markdown documents, with tools for import, search, and retrieval.