"How to update software" matching MCP servers:

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.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

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.

An MCP server implementation that enables interaction with the Unstructured API, providing tools to list, create, update, and manage sources, destinations, and workflows.

Connect your Sanity content to AI agents. Create, update, and explore structured content using Claude, Cursor, and VS Code via the Model Context Protocol. Transform content operations from complex queries to simple conversations—giving your team superpowers without sacrificing structure.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Advanced content gap analysis using Query Decomposition and Keyword Fan-Out (Google's patented methodology). Tells you exactly what user queries your content covers - and what it misses. Built on academic research because I needed to understand how AI search engines actually evaluate content.

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.

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 lab demonstrating how to deploy a Model Context Protocol (MCP) server and integrate it with Microsoft Copilot Studio, allowing users to connect AI models to different data sources and tools through a standardized protocol.

Enables agents to persist, search, and manage short- and long-term memories using local SQLite storage and offline embeddings, with tools for recall, update, confirmation, and consolidation without external services.

Enables control of Software Defined Radios and decoding of radio protocols through an AI-friendly Model Context Protocol interface, supporting RTL-SDR and HackRF hardware for signal analysis and protocol decoding.

An MCP server that provides jokes to Microsoft Copilot Studio, demonstrating how to deploy a Model Context Protocol server that delivers Chuck Norris and Dad jokes upon request.

Provides persistent semantic memory for AI agents via MCP, enabling them to remember, recall, list, update, and forget memories with vector-based similarity search.

An MCP server implementation that standardizes how AI applications access tools and context, providing a central hub that manages tool discovery, execution, and context management with a simplified configuration system.

A Model Context Protocol server that enables coding agents to search for and record software issue fixes with semantic search using natural language queries.

A production-grade MCP server that provides persistent long-term memory for AI agents using MongoDB, enabling them to store, search, update, delete, retrieve, and summarize structured project memories across developer workflows.

An autonomous AI software engineer and MCP server that uses LangGraph and a hybrid RAG index to autonomously edit, test, and debug codebases.

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.