"Reading terminal outputs from a Next.js dev server while developing in Cursor" matching MCP servers:

A template MCP server built with Next.js using the Vercel MCP Adapter. Provides a framework for deploying MCP servers with custom tools, prompts, and resources on Vercel with SSE transport support.

A FastMCP server for Cursor platform APIs enabling spend guardrails, cloud agent monitoring, and inter-agent inbox messaging.

An MCP server that exposes any REST API to LLMs through runtime discovery, providing tools to browse endpoints, fetch schemas, and apply business rules without hardcoding or schema duplication.

A personal MCP bridge that lets ChatGPT drive a local Codex CLI, translating instructions into Codex app-server threads and returning compact summaries of what Codex did.

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 Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

Enables an orchestrator like Claude Code to hand coding work off to Cursor SDK agents, run as detached background jobs that survive the session and can be listed, inspected, steered, resumed, or stopped. Agents are asynchronous and reusable across follow-up turns, letting expensive frontier models delegate cheaply without blocking or paying to read the results.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

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.

Enables any MCP client to triage cloud agents, projects, conversations, transcripts, pull requests, and files, including listing agents, reading transcripts, following runs, and sending follow-ups, using an API key or browser sign-in.

Enables AI agents like Codex and Claude Code to control shared Chrome tabs, offering site-specific tools such as browsing and posting on Reddit. Agents can navigate the visible page and execute actions directly in the same tab.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

Enables Claude Code to delegate tasks to Cursor's headless agent, run adversarial reviews, and verify findings with prosecutor/advocate roles.

Enables Grok Bot to securely communicate with a local Cursor Agent CLI through an OAuth-authenticated MCP gateway, exposing exactly two tools for asking questions and checking status.

Enables AI agents to control Cursor IDE's built-in Browser Tab through a stdio MCP interface, providing accessibility snapshots with refs, click/type/fill, wait conditions, screenshots, and console/network inspection.

Enables AI agents to request an independent second opinion from a different frontier model, with PII scrubbing before transit, live web search grounding, caching, and multi-provider failover.

Acts as a bridge between Claude's desktop application and the Cursor editor, enabling seamless AI-powered automation and multi-instance management across platforms with standardized communication and secure token-based authentication.

MCP server enabling Cursor to delegate complex coding tasks to ChatGPT Web (GPT-5.6 Sol High) via Temporary Chats, supporting parallel batch reviews and up to five concurrent sessions.

Enables users to record Windows desktop and browser workflows once and generate replayable Cursor Skills, using Playwright MCP and Windows Computer MCP for semantic, non-coordinate replay.

Enables users to describe an LLM fine-tuning workload once and receive ranked, task-aware GPU recommendations across a dozen cloud providers based on required VRAM/FLOPs, cost, and runtime. It surfaces the cheapest, fastest, and balanced options through MCP so coding assistants can plan training hardware without manual price comparison.