"Improving the Intelligence of Large Language Models" matching MCP servers:

Enables downloading real binary files from Egnyte and uploading large files with chunked support, complementing the official Egnyte MCP connector.

Enables intelligent handling of large files through smart chunking, search with regex support, line navigation, and streaming capabilities without loading entire files into memory.

Enables AI agents to perform workspace file operations (read/write/recover) with transaction-safe guarantees, durable recovery, and explicit human approval via a separate Broker.

Enables AI assistants to analyze, rename, categorize, and organize local documents using OCR, metadata extraction, and batch processing, with preview mode and undo support.

MCP server that prepares Proof of Invention (POI) transaction requests for submission

Provides bounded, read-only analysis of local project directories, enabling inventory, marker detection, and safe file reads without executing code.

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.

MCP server for sandboxed, read-only SQL queries on CSV/Parquet/JSON files via DuckDB, limited to a specified directory.

A production-grade Model Context Protocol server that enables secure management of context data through a React dashboard, supporting ZIP processing, context approval workflows, and system monitoring.

Enables users to ask natural-language questions about a local folder's contents, including file names, sizes, and timestamps, with rollups by type and age and cleanup suggestions for stale or large files. It returns metadata only and cannot read, move, or delete files.

Enables coding agents to extract verbatim, intent-relevant spans from large low-density files like logs, CSV, JSON, and HTML, verified against the original file to reduce context usage.

MCP server that converts file contents into compact, line-numbered PNG images for vision models to read, reducing token usage by roughly 7x for large files.

Navigate, search, and edit large codebases, logs, and data files that exceed AI context limits.

Enables management of BTRFS snapshots via Snapper and monitoring of BTRFS filesystem health through the Model Context Protocol.

Token-efficient skim-then-expand I/O for agentic models, enabling lossless reading of large files via skeletons and exact expansion with anchor IDs.

Enables AI agents to efficiently process large local and online PDFs through selective extraction of text, images, and metadata. It provides tools for content search and document outline navigation to optimize context window usage.

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.

locidx

Lets users shape Space Dust synthesizer presets by describing the sound they want in natural language, instead of turning knobs. It reads, creates, adjusts, and compares preset files, with safety rails and live updates.

Enables large language models to refine Office documents (Word, Excel, PowerPoint) via interactive visual selection of text and fine-grained prompt annotations in a browser window, with version history and precise location-based edits.