"Artifact Hub" matching MCP servers:

Bridges multiple CLI coding agents (Codex, Cursor, OpenCode, Claude, Antigravity) into any MCP client, enabling delegation of prompts, parallel execution, and code review workflows.

A centralized MCP hub for managing multiple coding agents across projects, enabling parallel, non-blocking dispatch and orchestration from any MCP-capable client.

Enables mobile control of Antigravity coding agents via Telegram, allowing task submission, plan approval, and artifact viewing.

An autonomous virtual software agency MCP server that generates production-grade software projects from a description, with 27 senior personas debating and cross-examining every artifact.

A multi-agent collaborative development bridge that enables TRAE Work to offload complex tasks to Codex executor, with tools for task submission, status tracking, artifact listing, and permission management.

Enables AI harnesses to delegate code analysis, modification, testing, and long-running tasks to the locally installed Antigravity CLI via stdio MCP, with job status tracking and conversation continuity.

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.

A unified hub for centrally managing and dynamically orchestrating multiple MCP servers/APIs into separate endpoints with flexible routing strategies.

Acts as a unified dispatch hub for local coding agent CLIs, supporting capability-based routing, schema validation, and retries via MCP tools.

This MCP server enables automated maintenance and code analysis for Python/pytest repositories in isolated Docker environments. It supports read-only investigations, fix-and-verify tasks, and provides full audit trails with SQLite event history and artifact exports.

A self-hosted remote MCP server that provides reusable prompts and development conventions across various AI tools. It features a modular architecture for organizing and namespacing custom prompts to streamline AI-assisted coding workflows.

Enables AI agents to develop within a local project workspace by reading and modifying files, running commands and tests, checking Git state, and persisting progress as history sessions that can be restored in later conversations.