"Finding people named Altman on LinkedIn" matching MCP servers:

Bridges the ChatGPT web UI to a local development environment over MCP, letting the model read and patch files, run real shell commands and terminals, execute tests, and control the desktop (screen, mouse, keyboard, clipboard) within user-approved folders. It adds parallel workers, goal/loop long-task control, plugin tools, and a security-hardening layer with risk-tiered shell policy, audit logging, and credential sanitization.

Connects ChatGPT to local files, terminals, and the desktop through MCP with Chrome integration, letting users read and edit project files, run commands, and follow tool results as they arrive. It also adds durable multi-agent workers, Goal/Loop task tracking, mid-task corrections, and Compact & Resume for carrying session and worker history into a fresh chat.

Enables ChatGPT to work on a local project by reading and editing files, running shell commands, keeping terminals and desktop sessions open, and splitting work across durable workers whose context persists between tasks. Long-running work can be steered mid-flight, tracked with Goal, and carried into fresh chats via Compact & Resume.

Lets ChatGPT work directly on a local project by reading and editing files, running shell commands and tests, keeping terminals open, and using the user's desktop, all paired with an approved workspace folder. It also splits independent jobs across persistent workers that retain context, while riding the user's existing ChatGPT plan rather than Codex quota.

YOUR AGENTS COLLIDE. File a FlightPlan. Run multiple coding agents without them stepping on each other. FlightPlan gives each agent the same preflight picture, lets them coordinate while plans are still cheap to change, and leaves behind what changed and why for whichever agent comes next. Across sessions. Across people. Across providers. Across time. One shared picture.

Enables handing one self-contained job to the Claude Code CLI on the local machine and getting back its final message plus a manifest of the artifacts it produced, without letting Claude write into the user's project. Reads are limited to named directories and all writes stay in a throwaway run directory, so the user reviews the listed files and copies in only what they approve.

Agent NERO is an MCP server that enables Claude Code to spawn, manage, and communicate with persistent, named LLM agents that maintain conversation history and can use tools like file I/O, command execution, and shared memory.

Enables delegating coding tasks to the Pi CLI through MCP with named sessions, synchronous or asynchronous execution, and a read-only transcript window on Windows.

Enables finding existing shadcn-format UI components by plain-language description instead of exact names, returning ranked matches with install commands and per-dimension match reasons.

Enables multiple AI coding agents to collaborate on a project by coordinating tasks, file leases, and messages through a shared hub, preventing conflicts and enabling parallel development.

Enables two-way collaboration between Claude Code and the Codex CLI: Claude can hand off plans for execution or adversarial review, while the working agent can block to ask Claude questions, report progress or findings, and receive mid-run corrections. Supports multiple named, resumable sessions per project with configurable model, effort, and sandbox settings.

Wraps OpenAI Codex CLI as an MCP server, exposing 8 Codex tools (exec, review, skill list, skill run, status, poll, list jobs, kill) as named tools for use with pi or codex.

A token-efficient, codebase-aware AI agent runtime that provides MCP tools for starting runs, getting run states, explaining code, and finding files.

Visual feedback for AI coding agents on Astro sites, providing live annotations with source file:line, CSS selector, Tailwind classes, and computed styles.

MCP server that lets multiple coding-agent sessions on the same machine discover each other and collaborate through a shared SQLite database.

A tiny always-on server that coordinates multiple coding-agent sessions on one project (a 'show'), using a worker/director pattern with polling, leases, and SQLite state.

A code understanding and generation system designed for Galacean Engine that helps LLMs comprehend the engine architecture and generate high-quality engine code based on user intent.

Plori runs AI agents in Workspaces. A Workspace is a project folder in the cloud. Several agents can work in it at the same time, each on its own copy of the files, and Plori merges their work back into the folder. Every version of the files is kept.

An MCP server giving coding agents context-window-aware code search and safe, atomic multi-file edits — built to cut token usage on large codebases without sacrificing correctness.

Enables AI coding agents to retrieve relevant execution context before a task, compare candidate paths, log outcomes, and search past experiences stored locally in SQLite. It lets agents reuse what worked on similar tasks and get live alerts when a run stagnates or repeats failures.