"Reactive Resume" matching MCP servers:

A resume MCP server that enables AI agents to search work history by keyword or meaning, retrieve detailed company info, and send scheduling emails, turning a static resume into interactive tools.

Exposes a personalized AI agent that reads your resume and provides intelligent responses about your professional background through a standardized MCP server interface with RAG capabilities.

Enables Claude to intelligently query and analyze your resume using RAG technology. Supports skill matching against job requirements and answering questions about your professional background from locally stored resume files.

Enables parsing of raw resume text into structured JSON format with organized sections for skills, experience, education, and projects. Uses Google's Gemini AI model to extract and categorize resume information from unstructured text input.

Crawl any website into clean Markdown, search through pages, read full content, and extract structured data using OpenAI, Claude, Gemini, or Grok — with auto-citation and resume support.

Overcomes Claude Code's 200k token context limit by implementing persistent long-term memory using Redis and AI-powered state summarization. Enables seamless checkpoint and resume of project context across sessions.

An MCP server for Claude Code that enables semantic search of past session transcripts and intelligent session forking, allowing users to find and resume from relevant previous conversations with full context.

Enables scoped project discovery, deterministic rule and profile management, and durable automation with trigger/pause/resume/cancel and audit history for approved project contexts.

Enables AI agents to index and search local skill libraries, persist and resume task checkpoints, and run validated workflow plans with controlled approval and write permissions.

Enables coding agents to explicitly save, resume, and search encrypted project memory locally, with opt-in disclosure and offline neural retrieval.

Local-first MCP server that persists AI coding agent memory and session context, enabling seamless resume across sessions with searchable memories and checkpoints.

Provides persistent cross-session AI memory, code graph analysis, and subagent context compression so coding agents can resume work instantly and avoid re-reading the codebase.

An AI-powered job recommender built on the Model Context Protocol that provides personalized career suggestions through resume and skills matching. It utilizes LLMs and external tool integrations to help users find relevant job openings based on their professional profile.

Enables recruiters to query a person's career, projects, and impact using natural language, acting as a living resume.

Provides file system access for resume matching through JSON-RPC, enabling semantic search and workflow orchestration with LangGraph.

AI-powered agentic workspace by Ambuj Systems for career intelligence, resume tailoring, and claim verification.

Enables AI assistants to query a person's CV and portfolio content via MCP tools and resources, returning grounded answers from local markdown data instead of relying on resume parsing.

An MCP server that retrieves resume/experience evidence relevant to a job description via vector RAG, and tracks fit-analysis results in a configurable tracking store (Notion or SQLite), with tools like match_job, push_to_tracker, and list_applications.

Enables AI coding sessions to store consented facts, edges, and provenance in a local SQLite graph and recall only the relevant connected neighborhood needed to resume work. It preserves decisions, constraints, and failures across chats without replaying entire transcripts.

Enables users to create and refine a research outline, queue report generation in the background, and retrieve progress or completed Markdown reports, with automatic checkpointing so unfinished work can resume after a restart.