"Secure Shell (SSH) Overview or Usage" matching MCP servers:

Deterministic context compression for MCP agents, reducing token usage via 11 tools for prompts, history, shell output, file deltas, and code navigation without ML or GPU.

A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.

tooltrim reduces the tokens agents spend re-reading bloated tool results. Run it as an MCP server exposing compress and expand_tool_output, or as a gateway in front of any upstream MCP server: it re-exposes the upstream tools unchanged and shrinks each result (HTML/JSON/logs/tables) before it reaches the model, keeping the relevant content only.

Self-hosted digests of your Telegram channels over MCP: AI summaries, the last digest and raw per-channel messages. Works with OpenAI, Anthropic or local Ollama.

Two self-hosted MCP servers: manage a local model machine (Ollama pull/switch, LoRA training) and bridge to local Ollama/vLLM for pure language processing tasks (writing, summarizing, classifying, extraction) without giving the calling agent tools or file access.

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.

Provides MCP-compatible AI clients with offline text analysis and rewriting tools, including statistics, extractive summaries, keywords, readability scores, case conversion, entity extraction, and diffing, all running locally without API keys or network calls.

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.

Enables context compression for Claude Code with Thai language support, reducing token usage by 50-88% while preserving full recoverability of original content.

Turns plain text or Markdown, web pages, and local PDFs into editable Markdown outlines that can be rendered as standalone, offline-capable interactive HTML mind maps. Works with any MCP client with no API key required, since the connected model writes the final outline from a draft and cleaned source text.

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.

MCP server for HumanPen that lets AI agents work on real documents (.docx, .pptx, .pdf) — humanizing content to lower AI-detection scores, converting citations, condensing, and translating while preserving formatting, tables, images, and citations.

An MCP server providing secure calculation, knowledge base search, and text statistics tools, along with resources and prompts for enterprise agent integration.

Fetches, parses, and summarizes ETSI telecommunications and media standards documents, enabling Claude to autonomously look up and ingest standards mid-conversation for answering questions, generating conformant code, or writing XML.

Enables an assistant to transcribe video or podcast recordings, then read, search, summarize, and manage the resulting transcripts through MCP tools.

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

MCP server that compresses markdown files into caveman-style prose, cutting token usage by 65% while preserving code and structure.

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.

Enables fetching and analyzing YouTube video comments and replies through the YouTube Data API. Supports sorting by relevance or time and returns structured JSON data for comment analysis, summarization, and translation tasks.

Analyzes YouTube videos using Google's Gemini API, allowing users to get summaries or ask questions about video content via direct URL input.