"Understanding and Building Probability Density Functions (PDF) Models" matching MCP servers:

Provides intelligent summarization capabilities through a clean, extensible architecture. Mainly built for solving AI agents issues on big repositories, where large files can eat up the context window.

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.

The local PDF workflow for Claude Desktop and MCP hosts: fill, sign, merge, split, extract, and analyze PDFs without sending files to a web app.

An MCP server that enables users to read, search, and analyze PDF documents. It provides tools for extracting text, viewing metadata, searching content with context, and generating word statistics.

An MCP server that provides a tool to extract text content from local PDF files, supporting both standard PDF reading and OCR capabilities with optional page selection.

A Model Context Protocol server for managing text notes, with resources (note:// URIs), tools (create_note), and prompts (summarize_notes).

A PDF processing server that extracts text via normal parsing or OCR, and retrieves images from PDF files through the MCP protocol with a built-in web debugger.

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

A local document processing toolkit for AI agents that extracts text, converts PDFs to Markdown, merges files, extracts tables, and summarizes documents without external API dependencies.

Enables reading and extracting content from PDF documents including text (as Markdown), images, tables, and metadata from both local files and URLs, with OCR support for scanned documents.

Enables comparison of baseline and counter-party contract clause revisions, flags high-risk shifts in indemnity, IP assignment, liability caps, and non-solicitation, and generates legal redline summaries with severity and escalation verdicts.

A Model Context Protocol server that enables Claude to fetch, process, and extract information from PDF documents, including LaTeX mathematical equations.

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.

A persistent document management and research assistant powered by Groq, enabling AI-driven document analysis, professional formatting, and long-term memory across sessions.

Enables Claude to read and analyze PDF documents with automatic OCR processing for scanned files. Features intelligent text extraction, caching for performance, and secure file access with search capabilities.

Enables scanning of enterprise MSAs, NDAs, and vendor SOWs to detect high-risk clauses such as unlimited indemnity, one-sided IP assignment, perpetual non-compete, and evergreen auto-renewal traps. Computes composite risk scores with an overall contract health rating and generates precise legal redlines and executive memos.

Enables comparing baseline and counter-party contract drafts to flag high-risk clause shifts and synthesize legal redline summaries.

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.

MCP server for Differino that lets AI agents compare PDF, DOCX, and TXT documents, with tools for visual and text diffs, summaries, exports, and account balance checks.

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.

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.

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.

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 server that integrates Google News and Google Trends, enabling AI models to fetch news, analyze search interest, calculate growth rates, and summarize articles.

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 that converts PDF files to Markdown format using AI sampling capabilities, supporting both local files and URLs with incremental conversion features.

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.

An MCP server that exports PDF documents to markdown format optimized for LLM processing.