"Configuration d'un compte professionnel sur les réseaux sociaux" matching MCP servers:

Automatically provides AI agents with proven instructions and past failure warnings for common tasks like deployment, auth, and payments, enabling flawless execution without manual configuration.

An MCP server that provides graph-aware tools for interacting with Little Big Brain knowledge graphs, enabling search, query, commit, and configuration operations via natural language.

A self-learning AI standards system that automatically detects patterns from user corrections and updates AI configuration files. Learns from repeated corrections (like 'use uv not pip') and automatically generates CLAUDE.md and other AI assistant configuration files.

Enables seamless integration with AnythingLLM instances, providing complete workspace management, chat operations, document handling, user administration, and AI agent configuration through natural language.

A Python library for storing and querying n-ary relationships with provenance tracking. SQLite-backed, zero configuration.

Provides shared user memory across Codex and Claude Code with project-aware routing, keeping unrelated projects in separate workspaces via a private configuration file.

Enables full read/write management of SAG knowledge bases, including source and document CRUD, semantic search, knowledge graph queries, and model configuration through 18 MCP tools.

Manages Claude Code configuration and provides cross-session intelligent memory via the Luoshu system, enabling Claude to remember decisions and project context across conversations.

A bridge between LLMs and OpenWebUI workspaces that enables managing projects, notes, and knowledge bases. It supports safe project configuration updates through a propose-evaluate-apply workflow and direct file uploads to knowledge bases.

Enables AI assistants to maintain persistent conversations and context between sessions through automated saving and global installation across projects. Provides zero-configuration memory persistence with automatic conversation history preservation.

An in-memory knowledge graph MCP server that gives coding agents structural and semantic recall over codebases by indexing Python source, ADR documents, and project configuration, exposing 7 tools for search, traversal, context retrieval, and natural-language Q&A.

A fork of the Neo4j Model Context Protocol server that enables interaction with Neo4j knowledge graphs through environment variable configuration and improved options for remote connections.

Provides 19 tools for discovery, data analysis, configuration, and simulation, enabling an agent to drive digital twin workflows with session persistence and knowledge graph capabilities.

Enables management of Amazon Bedrock Knowledge Bases including creation, data source configuration, document ingestion, and RAG (Retrieval-Augmented Generation) queries with support for multiple embedding models and custom parsing/chunking strategies.

Enables LLM agents to access a private knowledge base through MCP by automatically chunking and indexing .txt documents, with zero configuration and no Docker or vector database required.

Puts PyVISA in front of AI tools so they can discover and drive oscilloscopes and other SCPI instruments over LAN, USB, GPIB, and serial. Builds a per-instrument local knowledge base from identity, manuals, and past commands, then exposes tools for SCPI queries, measurements, waveform capture and export, and scope configuration.

Connects BookStack knowledge bases to Claude through 47+ tools covering complete CRUD operations for books, pages, chapters, shelves, users, search, attachments, and permissions. Enables full management of BookStack content and configuration through natural language.

Integrates Dify's external knowledge base API with the Model Context Protocol to enable AI agents to retrieve and query relevant information. It supports relevance scoring, metadata filtering, and flexible configuration through environment variables or command-line arguments.

Enables context optimization for LLM-powered coding assistants by compressing prompts, injecting task protocols, and providing a 2nd Brain knowledge graph with tools for prompt compression, knowledge retrieval, metrics, and runtime configuration.