"Developing a Bulk Email System" matching MCP servers:

Enables AI agents to control a Playwright-driven browser through natural-language instructions, including page interaction and live browser task execution.

Comprehensive quest orchestration system with KĀDI broker integration, multi-channel approval workflow, and Git-backed data storage.

基于MCP架构的微信公众号文章爬虫系统，支持AI智能体通过Selenium自动抓取微信公众号文章内容和图片，实现文章内容的智能分析。

A local MCP server that routes QA reviews through deterministic profiles and risk-based escalation while keeping source evidence and final decisions with the host.

An advanced MCP server that implements sophisticated sequential thinking using a coordinated team of specialized AI agents (Planner, Researcher, Analyzer, Critic, Synthesizer) to deeply analyze problems and provide high-quality, structured reasoning.

Enables real-time user intervention for MCP agents through a web UI, allowing users to review context and provide feedback when AI agents drift from intent, keeping them on track.

Discover and connect your agents with other agents via A2A protocol. Share resources for free or fee.

A soul document API for AI agents. Injects a short grounding preamble into your agent's system prompt before each session — licensing honest uncertainty, refusing cliché, and holding judgment instead of faking it. One call. ~200 tokens. Same model, better agent.

Gives AI agents their own email address with inbound parsing, classification, extraction, and prompt injection screening, plus tools to manage mailboxes, send/receive emails, and handle draft approval workflows.

Human-in-the-loop approval inbox for AI agents: an agent proposes an action (send email, post comment, run a command), a human approves, rejects, or edits it from a web, mobile, or Slack/Discord/Telegram inbox, and the agent only runs on approval. Full audit trail, self-hostable (MIT).

Provides AI agents with low-latency reflex tools for fast safety checks, option selection, verification, and scoring using calibrated probabilities, reducing reliance on slow LLM deliberation.

Enables AI agents to bulk-classify files, logs, and JSONL records against user-defined criteria via Jev, returning compact summaries and review queues for exceptions. It keeps bulk data out of the agent context and supports reproducible cost and quality benchmarks.

Enables Claude Code to offload bulk tasks to cheap OpenRouter worker models via tiered model selection, parallel batch execution, daily budget caps, and cost logging.

Lets MCP clients like Codex delegate independent sub-tasks to DeepSeek as a sub-agent via a single tool, with optional context, custom system prompts, and configurable model/temperature settings.

Enables collaboration across Claude, GPT, and Gemini through a unified MCP interface, with configurable execution policies for synthesizing or directly delivering worker outputs.

Enables a host AI agent to trigger a multi-agent debate, running N-rounds where agents critique each other's answers, then synthesizes a consensus recommendation with ranked options.

A local MCP proxy that connects AI clients to nomos system controllers, allowing registration and switching between them via natural language. Enables smart home control through MCP-compatible AI clients.

A Model Context Protocol (MCP) server that provides intelligent search capabilities for discovering relevant Claude Agent Skills using vector embeddings and semantic similarity. This server implements the same progressive disclosure architecture that Anthropic describes in their Agent Skills enginee

Lets separate coding-agent sessions coordinate through a shared, role-addressed mailbox instead of a human relaying every message, over a single local SQLite file or a hosted server with many isolated channels. Messages can be sent as tracked obligations that stay open until one side resolves and the other confirms, alongside threads, full-text search, work statuses, and an append-only versioned pin system where protected changes require recorded agreement from every declared role.

A persistent state machine and notification system for AI agents to manage complex, multi-step workflows via the Model Context Protocol, preventing context drift by maintaining structured checklists and sending desktop alerts.