"Information about creating or finding a resume" matching MCP servers:

MCP server for AI-powered resume optimization and generation. It enables clients to optimize existing resumes or generate new ones from background info via LLM, producing formatted .docx and .pdf files.

Enables to perform mathematical calculations, retrieve weather information, and manage files through a standardized MCP interface, showcasing best practices for building MCP servers.

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.

Enables Claude Desktop to act as an ATS resume checker: lists resume files and evaluates them against job descriptions, providing formatting audits and keyword match scores.

Enables local editing and compilation of LaTeX resumes, syncing with Overleaf via browser automation without Git or API access.

Tailor your CV to any job posting with ATS keyword scoring and clean PDF/DOCX export.

Compiles LaTeX resumes into PDFs via MCP tools, including validation, preview, template compilation, and LaTeX export, with security defaults like path isolation and no shell escapes.

An intelligent, zero-cost MCP server that securely parses local resumes and compares them against live job postings for ATS scoring, skill gap analysis, and interview prep.

MCP server for processing Markdown resumes into multiple formats (PDF, LinkedIn templates) with AI analysis and Claude integration.

Provides sandboxed file system tools (read, write, search, list, watch) over the Model Context Protocol, enabling resume matching and analysis workflows via an agent.

Provides tools to monitor host system health including CPU load, disk usage, and network status while enabling file system management tasks like searching and moving files. It includes built-in safety guards to prevent operations on critical system directories.

Provides MCP tools for filesystem operations on resume directories, including watch, batch extraction, and saving match results.

Enables AI assistants to read, search, and analyze local file systems with tools for reading file contents, listing directories, searching by patterns, and analyzing folder structures for context-aware queries.

Provides secure filesystem access for AI models through the Model Context Protocol with strict path validation, file operations, directory management, and system command execution within predefined directories.

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.

A self-hosted MCP server that enables AI coding agents to read, edit, search, and run code in local projects with human review loops and policy controls.

Enables interaction with the WhkerDB shared PDF editor to manage collaborative rooms, file structures, and real-time annotations. It allows users to upload PDFs and images, manage note trees, and sync data across platforms through natural language.

Provides a secure workspace for AI-driven file management and Python script execution designed to run on Vercel. It enables tools for full file operations and code execution within a sandboxed environment.

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.

Reads and searches local email archives (.mbox or .eml) with full MIME parsing, enabling queries about senders, subjects, dates, and bodies while keeping all data on your machine.