"A server for finding prompts" matching MCP servers:

A Model Context Protocol implementation for managing and serving AI prompts with a TypeScript-based architecture in a monorepo structure.

前端ai主导的全流程，自动化的预开发；旨在解决前端开发在联调前期的各种繁琐工作

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

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。

Enables remote control of the Cursor AI agent via chat platforms like GitHub Issues or Telegram, allowing users to start sessions and receive summaries and prompts in a chat thread.

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.

Multi-agent swarm orchestrator for AI workflows. Exposes blackboard read/write, agent dispatch, permission gating (AuthGuardian), token budget enforcement (FederatedBudget), and audit log tools over MCP. Supports 14 AI framework adapters including LangChain, AutoGen, CrewAI, Codex, and A2A — mix any frameworks in one swarm with race-condition-safe shared state.(Traffic light for agents)

BotGathering MCP server: a zero-dependency Node.js stdio server that implements 10 tools (ask_how_to, search_recipes, get_recipe, get_recipe_details, list_categories, register_bot, whoami, download_recipe, report_result, quick_report) for finding, running and reporting on tested step-by-step recipes for AI agents. It validates input, ranks recipes locally, formats results and checks reports for pe

AgentPay — x402 crypto data gateway on Stellar. 10 live pay-per-call tools: token prices, whale activity, gas tracker, DeFi TVL, Fear & Greed, Dune queries, token security. Agents pay USDC on Stellar. No API keys. Budget-aware sessions.

A local stdio MCP server for running single-turn prompts across multiple model providers (OpenAI, Ollama, OpenRouter), allowing agents to fan out the same prompt to several models and combine the output.

Enables MCP clients to drive a sandboxed Chromium container over CDP with tools for navigation, clicking, typing, selecting, key presses, scrolling, finding, text extraction, snapshots, and screenshots, optionally restricted by a host allowlist.

prevents llm from overload, freeze and oscillation

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.

Agent failure memory network. Search 235+ verified debugging lessons from real engineering sessions. Includes guided prompts for failure triage and release auditing.

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.

Generate deployment-ready AI agent system prompts for professionals in one click. 251 curated archetypes across 41 industries — from FX risk management to clinical documentation to OSINT intelligence.

Enables AI agents to choose the lowest-carbon cloud region for a workload, with tools for ranking regions by carbon intensity and latency, and for finding optimal start times to reduce emissions.

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