"Agent File Control MCP Server" matching MCP servers:

An HTTP MCP server that indexes large documents into exact-line-numbered sections, enabling AI models to locate, read, summarize, and edit parts of a document without ingesting the whole file.

An MCP server agent that enables analysis of YouTube videos by extracting transcripts, generating summaries, and creating chapter timestamps. It allows users to interact with video content through natural language to perform tasks such as writing social media posts.

Provides tools for text file analysis, including metrics like word counts and character frequencies, alongside file reading and directory browsing capabilities. This server enables LLMs to interact with and process local file content securely through the Model Context Protocol.

An MCP server exposing document search, retrieval, and summarization tools, paired with a LangChain agent that performs multi-step tasks like finding and summarizing relevant docs.

Enables local analysis of unstructured documents (PDF, DOCX, PPTX, SVG, PNG) by extracting text and structure with citation anchors, and verifies summaries against source material before a human approves saving a report.

science agent for literature search and summary

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.

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.

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

Enables analysis of clinical trial protocols using MCP tools for document listing, entity extraction, adverse event clustering, and summarization.

Analyzes unstructured documents in a local folder, extracting structure and key terms, and supports generating summaries via a host LLM with validated, approval-based report saving.

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.

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

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.

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.

Enables agents to parse resumes and job postings into structured OTP/OJP skeletons, validate them against JSON Schema, and extract agent-friendly summaries, all without LLM calls.

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.

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

Give your AI agent PDF summarization, code review, translation, web extraction, document comparison, and persistent memory — all paid autonomously via Bitcoin Lightning.

Enables delegating checkable subtasks such as summarization, search-and-summarize, and file downloads to a local OpenAI-compatible model, with mandatory result verification and job statistics.

A Python MCP server for web research that enables fetching web pages, searching, batch-fetching, extracting links, and summarizing content via client-side sampling, with optional local file access.

Enables Codex to delegate bounded, low-risk text work such as extraction, summarization, translation, and code drafts to a local WorkBuddy installation, returning small inspectable drafts for the main agent to verify.

Local MCP server that enables delegating low-risk tasks like summarization or code patches to a low-cost model, with the main agent reviewing results.

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.

A comprehensive Model Context Protocol server for content summarization that supports web scraping, file reading, content summarization, and topic-based summarization features.

Enables coding agents like Claude Code and Codex to offload boilerplate generation, summarization, and other bounded text tasks to local or cheap cloud LLMs, keeping the frontier agent in charge of judgment and code edits.

A local-first MCP server that lets agents search and summarize a user's own WeChat history, with stable pagination, bulk chat workflows, unread/event queries, and gated enrichment tools.