"A tool for semantically searching job postings on LinkedIn and Indeed" matching MCP servers:

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.

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 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.

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.

Enables AI agents to publish posts, images, comments, and reactions to LinkedIn as the authenticated user, with built-in safety features like daily budgets and deduplication.

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

Enables an AI agent to fully operate a LinkedIn account—posting, messaging, profile edits, and network management—with self-healing selectors, human-like pacing, and approval-based planning to avoid breakage or lockouts.

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

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.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

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.

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.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Self-host a realtime voice-call companion for coding agents. Exposes an MCP endpoint that agents can poll as an alternate input stream.

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.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

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.

A Model Context Protocol server that provides Claude with a dedicated space for structured thinking during complex problem-solving tasks, helping improve its reasoning capabilities.