"Understanding Requirement Analysis" matching MCP servers:

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.

Enables an agent to inspect an audio folder and report what is inside each file — duration, sample rate, channels, codec and bitrate — and to group byte-identical files, identical decoded audio stored in different containers, and near-duplicate candidates, with suggested keeps and explicit differences, all read-only.

Assists in Advanced Persistent Threat malware analysis by providing tools to securely download samples via jump servers using SSH and SCP. It enables automated retrieval of remote files through a multi-hop configuration for analysis workflows.

Enables forensic analysis of Windows notification databases (wpndatabase.db) from E01 forensic images, automatically extracting and parsing notification data with search, filtering, timeline analysis, and sensitive information detection capabilities.

Enables intelligent analysis and organization of image collections with smart filename generation, metadata extraction, and automated folder organization. Supports batch processing, color analysis, EXIF data extraction, and multiple naming styles for efficient photo management.

Enables extracting text, structure, and image data from unstructured documents (PDF, DOCX, PPTX, SVG, PNG, JPG) and scanning folder trees.

Enables read-only scanning and text extraction from PDF, DOCX, PPTX, SVG, and PNG files in a local folder, providing the raw text to AI models for summarization or analysis without an external LLM API.

Enables LLM hosts like Claude Code or Codex CLI to inspect folder structures and read unstructured documents (PDF, PPTX, DOCX, SVG, and images) by returning raw text and images for the host to summarize or analyze.

A simple Model Context Protocol (MCP) server that allows LLMs to create and manage user entries in a JSON file system database.

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.

Enables sales data analysis from CSV files using MCP tools, resources, and prompts, including metrics like revenue, profit, top products, and monthly trends.

Enables local folder analysis of unstructured documents (PDF, DOCX, PPTX, TXT, SVG, PNG, CSV, XLSX) by extracting structure, reading content, and generating reports, with a strict approval gate before any save operation.

A proof-of-concept MCP server that enables reading local files and performing basic arithmetic operations. It provides a simple foundation for understanding how tools are exposed to MCP clients.

An MCP server that enables analysis of local media files, including video, audio, and documents, by uploading them to EnriProxy for server-side extraction. It supports various complex file formats and provides structured model analysis for media types often unsupported by standard MCP clients.

The most feature-complete MCP server for Obsidian vaults. 23 tools and 3 resources for search, read, write, tags, link analysis, graph traversal, and canvas support.

Local MCP server that manages a job search using Markdown files, enabling skills tracking, gap analysis, and offer pipeline management via Claude.

Enables interaction with Telegram chat history, including text, photos, and documents, via the GramJS MTProto userbot. It provides tools for searching chats, syncing message history, and downloading media files for local analysis.

Enables local forensic analysis of files by orchestrating system binaries (file, exiftool, strings, Volatility) via safe subprocess execution.

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

Enables read-only analysis of local unstructured documents by scanning a folder, extracting text and structural metadata, and passing content with truncation and error-awareness to an LLM for summarization.