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

Enables LLM agents to explore and search a 1C:Enterprise configuration codebase, including metadata objects, module procedures, call graphs, and semantic similarity search.

A Multi-Agent Conversation Protocol server for interacting with Google Play Game Services Configuration API, allowing management of game configurations through natural language commands.

An MCP server integrating Perplexity AI's API to offer advanced search capabilities with support for multiple models and result configuration.

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

Enables managing ChipsBot chatbots, conversations, documents, RAG configuration, bot-to-bot routing, and AI models through natural language.

A server for the Machine Chat Protocol (MCP) that provides a YAML-based configuration system for LLM applications, allowing users to define resources, tools, and prompts without writing code.

Enables dynamic discovery, search, and on-demand retrieval of local Claude Skills, making them accessible as a composable skill registry across MCP-compatible hosts without manual configuration.

Low-token MCP server for HPE Networking automation, enabling search of Aruba/HPE docs, OpenAPI details, Central health checks, troubleshooting workflows, configuration management, and guarded ArubaOS 8 migrations and GreenLake Platform operations across platforms like Aruba Central, ClearPass, Mist, and Apstra.

Provides constraint satisfaction and optimization capabilities to LLMs and AI agents for scheduling, resource allocation, routing, budget optimization, and configuration problems using Google OR-Tools CP-SAT solver.

Enables natural-language search, page reading, deployment configuration, and build-log diagnosis over Liara cloud documentation, with a bundled corpus and no database or API key required.

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.

Enables read-only access to a local-first catalog of portable agent skills, exposing tools to discover ranked matches, inspect stored artifacts, traverse declared relationships, and view configuration.

Enables easy gRPC requests and Protocol Buffer file information retrieval through natural language commands. Supports unary RPCs with SSL, timeout configuration, and response time statistics.

Enables secure execution of Ditto DQL (Data Query Language) queries over HTTPS with safety checks and capability gating. Supports parameterized queries, health checks, and configuration management for Ditto database operations.

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 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.

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

Enables MCP clients to administer a local LM Studio instance: loading, estimating, downloading, configuring and unloading models, running chat and embedding inference, diagnosing the server, engines and hardware, and managing MCP connectors with conflict-safe configuration. It also builds a local document index supporting citation-checked search and answers grounded in TXT, Markdown, PDF and DOCX files.

Enables dynamic database querying through natural language questions using LLM-powered parameter extraction and template-based SQL generation. Supports flexible configuration for various domains and databases with automated response formatting.