"How to Control Tuya Smart IoT Devices" matching MCP servers:

A local-first MCP server that enables semantic search over PDF and DOCX documents using structure-aware parsing and vector storage. It allows users to query their local knowledge base through Claude Code without cloud dependencies or GPU requirements.

Generates complex draw.io diagrams (tables, kanbans, GANTT) using a local 9B LLM with RAG, featuring a real-time dashboard and CI/CD pipeline.

An MCP server for Claude Code that enables semantic search of past session transcripts and intelligent session forking, allowing users to find and resume from relevant previous conversations with full context.

Enables semantic search and knowledge graph exploration of Obsidian vaults using Smart Connections embeddings. Provides intelligent note discovery, similarity search, and connection mapping through natural language queries.

A read-only MCP server that enables semantic search of your Obsidian vault using Smart Connections embeddings, with path validation and security.

Gives Claude Code read-only semantic search over an Obsidian vault by reading the embedding files the Smart Connections plugin already produces, so no API key or network is needed at query time and it works across the WSL/Windows boundary. Exposes tools for meaning-based note and block search with folder scoping, related-note discovery, note reading, and index health checks.

Enables Claude to perform semantic search across your Obsidian vault using Smart Connections vector database. Provides meaning-based search, related note discovery, and context retrieval for RAG queries instead of basic keyword matching.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

An enhanced MCP server that fetches prompts from GitHub repositories with intelligent discovery, composition, and management features.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

Enables semantic search and related-note discovery across an Obsidian vault via MCP, providing RAG context blocks for natural language queries.

Enables management and troubleshooting of IoT devices through MCP tools, including real-time state analysis, fault case handling, knowledge ingestion, and retrieval-augmented insights.

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

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Provides semantic search, note retrieval, source explanation, daily digests, and health checks for a local Obsidian vault, enabling MCP clients like Claude Desktop and Claude Code to query the second brain via natural language.

MCP server that exposes hardware automation tools (build, flash, serial capture, symbolization, relay control) for ESP32-S3 boards, with board-level concurrency locks and remote access.

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.