"MiniMax MCP (Model Context Protocol) GitHub Repository" matching MCP servers:

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

A task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.

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.

The URL-Context-MCP MCP Server provides a tool to analyze and summarize the content of URLs using Google Gemini's URL Context capability via the Gemini API. Now also supports optional grounding with Google Search alongside URL Context. The server is designed to follow prompt-only orchestration: con

A simple notes MCP server that enables creating, listing, and summarizing text notes via resources, tools, and prompts.

Compresses, deduplicates, and filters tool outputs to optimize token usage and context window for AI agents.

Enables GitHub-shaped tool calls to list issues, retrieve pull requests, and summarize them, while refusing merge-via-apply operations and remaining a local proof rather than making production changes.

Provides a bulk_read_files tool that reads specified files and forwards them to a remote backend for summarization, so agents receive concise summaries instead of raw file contents.

Summarizes GitHub discussions, issues, and pull requests by storing data locally for fast, token-efficient responses.

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 long-horizon autonomous agents to avoid context-window exhaustion by enforcing configurable token budgets and distilling older conversation turns into concise sliding summaries.

Enables autonomous agents to keep long-running conversations within configurable token budgets by compacting message arrays, preserving system instructions and recent turns while distilling older history into concise summaries. This prevents context window exhaustion without external LLM dependencies.

Enables AI assistants to save, load, and search conversation context with AI-powered summarization and auto-tagging. Demonstrates semantic intent patterns and hexagonal architecture for maintainable AI-assisted development.

A TypeScript-based MCP server that enables users to manage text notes with features like note creation and summary generation using a Model Context Protocol.

Provides clean, bounded web content to AI by filtering HTML noise and optionally summarizing, preventing context window overflow.

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

Lets coding agents such as Claude Code and Codex hand summarising, field extraction, classification and OCR to the free, offline on-device model for Apple Foundation Models on macOS 27. One Rust binary, installed with Homebrew.

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.

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.

An MCP server that provides local, private, synchronous access to Ollama models for prompt answering, text classification, and model listing, without any file access or command execution.

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.

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 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.

Delegates mechanical text-to-text tasks to a local LLM to save Claude subscription quota, reading files server-side so large content never enters Claude's context.

Generates natural, fluent Chinese content that reads like human writing using the Qwen3-Next-80B-A3B model. Supports web search for latest information and direct file saving with customizable writing rules.

Enables an LLM to keep a persistent scratchpad by adding and reading notes, retrieving the latest note as context, and generating summaries through MCP.

Enables coding agents to offload reading and summarizing multiple large files to a cheaper worker LLM, keeping full file contents out of the main agent's context.