"Extracting data from GitHub" matching MCP servers:

A server that allows AI assistants to browse and read files from specified GitHub repositories, providing access to repository contents via the Model Context Protocol.

MCP server for translating PDFs using pdf2zh-next, preserving document context by extracting all text segments for LLM translation at once.

Local MCP server that registers restricted Python filters and runs them against local JSON, YAML, and TXT files, enabling safe data filtering and file conversion.

MCP server that profiles local data files (CSV, Parquet, JSON, Excel) and returns compact structured summaries with data-quality flags, enabling AI agents to understand datasets without seeing raw rows.

MCP server for parsing .eml email files, extracting metadata, content, and attachments with smart organization into folders. Enables AI to read and handle email files offline without triggering trackers.

A local MCP server that generates bullet points for resume project experience from GitHub repository signals, merges them into a PDF resume, and exports a new PDF using templates.

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.

Enables reading and extracting text from local documents (PDF, Word, Excel, PowerPoint, HWP, Markdown, CSV, etc.) without network access, and provides approval-gated summary saving and file organization.

Converts PDF files from local storage or URLs to structured Markdown format using Mistral AI's OCR API, preserving document structure and extracting images.

Automates internal system form submissions by reading data from Excel/CSV or Google Sheets and using Playwright to navigate menus and fill form fields, with support for both visible and headless modes.

An MCP server that enables AI assistants to read, search, and analyze PDF files from local paths or URLs. It provides tools for extracting specific page ranges, searching for terms, and retrieving document metadata.

A comprehensive tool server for reading, merging, and extracting content from PDF files via local paths or direct URLs. It enables metadata retrieval, regex searching, and page-specific text extraction with built-in caching and workspace-restricted security.

Enables AI agents to process and inspect PDFs by detecting document type, extracting text, and generating Markdown with layout information.

Enables accessing and extracting files from remote zip archives over HTTP, HTTPS, and FTP without downloading the entire archive. Supports listing files, extracting individual files, and getting file information using partial reading techniques.

Provides LLMs with secure, read-only access to local documentation by scanning directories, extracting content from PDF, DOCX, Markdown, and text files, and performing keyword searches.

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.

MCP server for privacy-gated local Apple data access including Mail, Messages, Notes, Calendar, Contacts, Photos, Reminders, Voice Memos, and iCloud Drive.

Enables AI assistants to read, create, merge, split, and edit PDF files through natural language commands in GitHub Copilot Chat.

A Model Context Protocol server integrating DevOps tools for GitHub, Docker, and filesystem operations.

A Model Context Protocol server that enables listing and reading PDF documents from a configured directory, extracting text content for MCP clients like Claude Desktop.