Loading…
Fullstack MCP Hub — Complete MCP toolkit with built in RAG, GUI and 50+ free tools (no paid APIs) that runs out-of-the-box. Works with Gemini CLI, OpenAI/Codex CLI, ChatGPT (dev mode), Claude, local LLMs, and anything that can connect to an MCP server—basically “Claude-level” tool power for local models.
Fullstack_MCP_hub is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 33/100. Fullstack MCP Hub — Complete MCP toolkit with built in RAG, GUI and 50+ free tools (no paid APIs) that runs out-of-the-box. Works with Gemini CLI, OpenAI/Codex. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding Fullstack_MCP_hub to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/jbulger82/Fullstack_MCP_hub.gitReview the upstream source before running this command.
Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "jbulger82/Fullstack_MCP_hub"
}Fullstack MCP Hub — Complete MCP toolkit with built in RAG, GUI and 50+ free tools (no paid APIs) that runs out-of-the-box. Works with Gemini CLI, OpenAI/Codex CLI, ChatGPT (dev mode), Claude, local LLMs, and anything that can connect to an MCP server—basically “Claude-level” tool power for local models.