"An application for finding emails in my mailbox" matching MCP servers:

Enables AI agents to interact with emails captured by a local SMTP mail sink, supporting listing, reading, clearing, and waiting for messages.

Cross-CLI agent messaging: let Claude Code / Codex / Pi / ZCode terminal agents message each other like email. File-based mailboxes with a resident delivery hub that picks the channel per recipient (tmux injection / native queue / daemon queue / pull), federated across hosts over ssh.

Enables an AI agent to pack a local folder and deploy it as a live static website at a public URL, with no account required for the first publish. Also supports checking site activation, listing existing sites with their status and views, and recovering access to a site whose key was lost.

A template MCP server that provides a basic structure and configuration for building Model Context Protocol servers. Includes Docker support, environment configuration, and examples for both STDIO and SSE transport modes.

Turns local Codex session transcripts into a searchable memory that MCP-compatible coding agents can query to retrieve relevant past sessions or exact commands and fixes.

An extensible TypeScript-based MCP server designed for Claude Code with a modular architecture for easily adding custom tools. It includes built-in examples like a calculator and echo tool, utilizing Zod for robust input validation and error handling.

Enables any MCP client to drive the user's real Chrome profile through a Chrome extension bridge, with tools for navigation, accessibility snapshots, ref-based interactions, console/network reading, screenshots, tab management, and restricted script evaluation.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.

Enables users to describe an LLM fine-tuning workload and receive ranked, task-aware GPU recommendations by estimating required VRAM/FLOPs and comparing live pricing across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.

Enables users to describe an LLM fine-tuning workload once and receive ranked cloud GPU recommendations with estimated VRAM/FLOPs requirements, cost, runtime, and data-source transparency.

Enables users to describe an LLM fine-tuning workload and receive VRAM/FLOPs estimates plus ranked GPU recommendations across multiple cloud providers, optimizing for cost, speed, or balance.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs calculations with cheapest, fastest, and balanced GPU recommendations across a dozen cloud providers. It supports CLI, REST API, and natural-language MCP integration without manual price comparison.

Enables users to describe an LLM fine-tuning workload and automatically estimate VRAM/FLOPs to recommend the cheapest, fastest, and balanced GPU options across multiple cloud providers. It supports natural-language requests through MCP, returning structured ranked hardware recommendations without manual price comparison.

Enables Codex to trigger an animated hand on your desktop that delivers playful cartoon slaps when mistakes are detected, with severity-based slap counts, while the agent continues working.

Stop Package Chaos: Unveil Your Mac’s Setup for Targeted ML Debugging

Tell your AI agent what you want a model to do. It searches Hugging Face for each part of the job and tells you which parts already exist there: which are an exact fit, which to fine-tune, which to train, and which are just code. Every model it names is real, current, and sized for your hardware.

A TypeScript MCP server boilerplate that provides example tools (calculator, greet) and resources (system info) to kickstart custom MCP server development.

An MCP server that enables AI to interact with the private 'my-design' React component library and design tokens for UI generation and technical support. It provides tools for component searching, API documentation retrieval, and migration guidance based on specific internal design specifications.

Enables MCP-capable IDEs to search 8,214 design-system components, retrieve exact source bundles and semantic tokens, and follow a copy-first handoff workflow with composition validation.

MCP server that exposes code review tasks from the Fix My Comments VS Code extension to AI agents, enabling them to list, retrieve, reply to, and update task statuses.

Provides a single MCP tool that returns the current time for a requested timezone, connectable to Claude Code and Claude desktop over stdio or Streamable HTTP.

An educational MCP server demonstrating tools, resources, and prompts for learning the Model Context Protocol.

A simple MCP server that provides current time in Korean timezone (KST) with multiple format options and generates random numbers within customizable ranges.

A secure file management MCP server enabling file operations like listing, reading, creating, renaming, moving, copying, deleting, and searching within a restricted workspace.

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.