"How to retrieve data from Power BI" matching MCP servers:

MCP server that converts PDF, video, web, and audio inputs into structured Markdown notes with support for checkpointing, batch processing, and Obsidian integration.

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

Enables AI language models to interact with YouTube content through a standardized interface, providing tools for retrieving video information, transcripts, channel analytics, and trend analysis.

MCP server for evidence-based bullet point summarization guidance. Validates and improves bullet lists using scientifically-validated principles from cognitive psychology and UX research.

A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.

Enables LLMs to interact with Fathom.video API for managing meeting recordings, retrieving transcripts and AI-generated summaries, searching meeting content, and accessing analytics and team data.

Enables AI agents and MCP clients to browse Roomtone meeting notes, add YouTube or pasted-text sources, generate summaries, ask questions answered from transcripts, and retrieve raw transcript text. Recording itself stays in the Roomtone app, so the server only reads and adds to existing meetings.

Unofficial MCP server for working with Kagi without API access (you'll need to be a customer, tho). Searches and summarizes. Uses Kagi session token for easy authentication.

Exposes support case reasoning tools — generate structured case briefs and check escalation readiness from any MCP-compatible AI client.

MCP server for extracting links from text dumps, checking duplicates, extracting YouTube transcripts (with fallbacks including speech-to-text), and preparing workspace-ready page payloads for your note-taking or saving tools. It does not write to your workspace; it hands off prepared content to your existing tools.

Enables any LLM to process arbitrarily long contexts through recursive decomposition, without requiring external LLM APIs.

Enables users to extract and review Claude's plan reasoning in Claude Code by reading plan files and providing a structured template for decisions, tradeoffs, and assumptions.

Enables transcription, summarization, and action item extraction from audio files on your Mac using MacWhisper and Claude Desktop, all locally without any cloud APIs.

Enables agents to check and retrieve concept-structured YouTube video summaries from the oratilo library via lookup and full-text search tools, avoiding redundant summarization work.

Automates Grammarly's web interface to check AI detection and plagiarism scores, then uses Claude to iteratively rewrite text until it meets target thresholds for humanized content.

Delegates replaceable grunt work (boilerplate, formatting, translation, long-document summarizing) from a premium agent to cheap models behind a local LiteLLM proxy, auto-routing each task by type. Delegated calls run in a separate process, so the subscription session and the API credentials never share an environment.

Fetches RSS feeds from tech blogs and news sites and returns AI-generated summaries via Claude.

Enables AI agents to search, watch, summarize, clip, and extract transcripts from YouTube videos, all without needing an API key or leaving the chat.

Unofficial MCP server wrapping crawl4ai that enables extraction and analysis of content from web pages, PDFs, Office documents, YouTube videos, and more, with AI-powered summarization and Google search integration to reduce token usage while preserving key information.

Provides 14+ character-level text analysis tools that give LLMs the ability to accurately count letters, analyze individual characters, and work with text at the character level—overcoming tokenization limitations.