"JobSpy job search tool or library" matching MCP servers:

An MCP server that exposes job-search and application-management capabilities to compatible AI clients, enabling discovery of vacancies, drafting of tailored application materials, and coordinated human-approved submissions.

An AI-powered job search and application assistant that enables multi-source job hunting, OpenAI matching, cover letter generation, and hybrid application automation via Playwright, Chrome CDP, screen OCR, and manual assist.

Profile-driven job search engine that aggregates postings, scores them against a user's profile, filters by eligibility (location, timezone, work authorization), and exposes find_jobs, list_sources, and describe_engine to local AI agents over MCP.

Enables MCP clients to submit long-running jobs that are executed safely in isolated child processes with a durable SQLite queue, configurable timeouts, retries with backoff, and backpressure.

A server built on mcp-framework that enables integration with Claude Desktop through the Model Context Protocol.

Provides a 'reflect' tool that creates cognitive checkpoints for AI assistants, forcing structured step-by-step reasoning through complex problems to improve accuracy and maintain context during task execution.

This server enables remote job discovery, tailored CV creation, and approval-gated application lifecycle management. It prioritizes safe, factual job applications without authenticated LinkedIn scraping or silent submissions.

An MCP server that enables users to manage, search, and apply specialized AI personas within Claude Desktop and Claude Code CLI. It features a local-first architecture with YAML-based storage for organizing curated prompts and role collections.

This MCP server enables job intelligence retrieval and ranking, providing tools to list jobs, fetch details, access corpus statistics, and score candidates against job descriptions using staged retrieval to optimize LLM context usage.

Provides a no-op 'think' tool that enables Claude to decompose complex problems and enhance reasoning by pausing to organize thoughts.

Implements Anthropic's 'think' tool for Claude, providing a dedicated space for structured reasoning during complex problem-solving tasks that improves performance in reasoning chains and policy adherence.

An MCP server implementation of Anthropic's Think Tool prompt engineering technique that enables Claude to break down complex problems and enhance its reasoning capabilities by providing a simple tool that echoes back thoughts.

An MCP server that enables AI agents to evaluate candidate-job fit by calculating weighted match scores based on skills and requirements.

This is a powerful Model Context Protocol (MCP) server that integrates multiple AI coding agents—Anthropic Claude Code, OpenAI Codex, and Google Gemini—directly into your workflow. It enables seamless cross-provider analysis, leveraging Gemini's massive token window, Codex's specialized coding capabilities, and Claude's advanced reasoning.

MCP server that builds itself by creating new tools as needed based on user requests (Requires restart of Claude Desktop to use newly created tools).

A Model Context Protocol server that enables AI assistants to interact with Google Gemini CLI, allowing them to leverage Gemini's large token window for analyzing files and codebases using natural language commands.

Enables on-demand generation of TypeScript tools using an LLM with human-in-the-loop approval and safe sandboxed execution. It allows users to create and persist custom functionality through natural language requests.

An MCP server that implements the 'think' tool, providing Claude with a dedicated space for structured thinking during complex problem-solving tasks to improve reasoning capabilities.

Filters and blocks specific tools from upstream MCP servers using regex patterns to reduce context pollution and remove unwanted tools from AI agent interactions.

A secure tool-execution plane for agentic AI that enforces JWT authentication, rate limiting, prompt-injection inspection, and audit logging, while ingesting downstream OpenAPI endpoints as MCP tools.