"Accessing Facebook Settings and Preferences" matching MCP servers:

This read-only MCP Server allows you to connect to Facebook data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

A multi-tool AI assistant system that uses Model Context Protocol to connect language models with various tools, including math calculations and weather information.

Provides structured dealer brand data including inventory, promotions, reviews, and dealer profile via MCP tools, enabling LLMs to answer accurate brand-related queries.

A meta-MCP server that acts as a universal gateway, allowing users to discover and execute tools from thousands of other MCP servers through semantic search. It dynamically loads servers on demand and provides standardized functions for searching, discovering, and running tools across the entire MCP ecosystem.

Provides persistent, self-optimizing memory for AI agents, enabling them to remember preferences and context across sessions and share knowledge across multiple agents.

Provides persistent local memory for any MCP-compatible AI agent, enabling agents to store, search, recall, forget, export, and manage preferences, rules, and facts with zero dependencies, no cloud, and no API keys.

Enables AI applications to add, search, update, and delete long-term memories using the Mem0 Memory API, allowing agents to persistently remember user preferences, conversation history, and contextual information across sessions.

Local MCP server that turns Mark Builds Brands' YouTube knowledge into an AI coaching assistant for ecommerce and Facebook Ads.

Enables managing and controlling AnythingLLM instances through MCP, providing tools to list, create, and delete workspaces, retrieve system environment settings, and get vector counts.

Provides long-term memory storage for AI assistants with semantic search, enabling persistent storage of preferences, decisions, and context with relationship tracking between memories.

A full-featured long-term memory system for Claude Code that persistently stores and retrieves preferences, decisions, and project context across sessions using hybrid search and LLM-powered extraction.

Intercepts Claude conversations via MITM proxy and stores them in Mem0, enabling Claude to search and recall past conversations, learn user preferences, and maintain context across sessions.

Enables LLMs to perform web searches through Perplexica with multiple focus modes (web, academic, YouTube, Reddit) and optimization settings, returning AI-generated responses with source citations.

This server enables users to download entire websites and their assets for offline access, supporting configurable depth and concurrency settings.

Enables AI assistants to interact with Meilisearch through a standardized interface, supporting index and document management, search capabilities, settings configuration, task monitoring, and experimental vector search.

Long-term memory for Claude Code, Cursor, and MCP clients with zero-config embedded PostgreSQL. Stores and retrieves facts, preferences, and decisions using 4-channel semantic, BM25, entity, and temporal search with cross-encoder reranking.

Provides AI assistants with a persistent knowledge graph and experience memory, featuring context-aware routing, progressive disclosure, decay-based forgetting, and automatic promotion of important knowledge. It offers 22 MCP tools for storing, recalling, connecting, and querying knowledge, experiences, preferences, and ideas.

Provides infinite long-term memory for AI agents with persistent, searchable storage of project details, preferences, and snippets. Reduces token costs by retrieving only relevant memories while keeping all data stored locally.

Provides persistent cognitive memory for AI coding agents, enabling them to recall architecture decisions, coding preferences, and project facts across sessions.