"Understanding Perl, Language Server Protocol (LSP), and Abstract Syntax Trees (AST)" matching MCP servers:

Enables generation of Satori syntax templates for creating engaging 140-character X (Twitter) posts. Supports five different structure types including basic forms, contrarian takes, news-based content, shocking news, and step-by-step guides.

Summarizes Slack activity and GitLab merge requests with OpenAI to help generate daily standups, weekly reports, or quarterly check-ins.

Enables exploring Steam store reviews and community discussion threads with server-side filtering, temporal analysis, and metadata context for understanding player sentiment and feedback beyond store page noise.

Model Context Protocol server for Archive of Our Own: search fanfiction and have an AI read and rank fics for you.

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.

Search and discover YouTube channels via natural language, track them, and summarize videos without transcripts using Gemini API.

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.

Lets any AI agent score and simplify its own text before it reaches a human, using Flesch readability metrics and plain-language rewrites entirely on the local machine.

A Model Context Protocol server that distills documents from multiple sources into HTML articles and Obsidian notes, with features like key element detection, image filtering, and async task management.

MCP server for RSS aggregation and LLM summarization, allowing users to fetch latest articles, search archives, and generate topic-specific digests via natural language.

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.

An MCP (Model Context Protocol) server specialized in deciphering PDF internal structures.

Model Context Protocol server exposing the bankstatementparser library as first-class agent tools for reading bank statements.

Enables natural language interaction with local .docx files, allowing users to find, read, search, and summarize Word documents using friendly names and location hints.

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.

MCP server that fetches YouTube video transcripts and optionally summarizes them. Supports multiple transcript formats (text, JSON, SRT, WebVTT), multi-language retrieval, and flexible YouTube URL parsing.

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

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.

MCP Long Context Reader is a Python-based toolkit designed to overcome the context window limitations and high costs associated with Large Language Models (LLMs) processing extensive documents. It provides a FastMCP server with multiple, powerful strategies for an LLM agent to 'read' and query long documents without needing to load the entire text into its context window.

Enables fetching, searching, and summarizing YouTube video transcripts with multi-language support.

A Model Context Protocol server that enables AI assistants to translate and rephrase text between numerous languages using the DeepL API.

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.

MCP server that converts video links into AI-generated Markdown notes, with tools for task management, transcription engines, and LLM providers.

Enables AI-driven analysis of aggregated news and hot topics from 35+ platforms, providing tools for trend tracking, sentiment analysis, and similar-content retrieval through natural language conversations.

Enables AI agents to read and analyze PDF documents for natural language Q\&A. Supports multiple LLM providers including Google Gemini, Anthropic Claude, and OpenAI.

Provides access to Whissle AI services for speech-to-text, speaker diarization, translation, and text summarization. It enables users to process various audio formats and manage text content through natural language tools.

An MCP server that enables web searching, URL content extraction, and summarization without requiring API keys. It also provides advanced mathematical evaluation and multi-language Wikipedia summary retrieval tools.

Enables managing documents on the filesystem through natural language, with tools for read, create, edit, delete, and commands for summarize, format, rewrite, and convert.

Precise character-level string indexing for LLMs. Provides tools for finding, extracting, and manipulating text by exact character position to solve position-based operations.

distill-mcp-v2 is a high-performance, network-dependency-free Python FastMCP server designed to aggressively optimize Large Language Model (LLM) context windows. It provides specialized tools for compressing and analyzing massive AI-agent payloads without losing critical semantic information.