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Give any AI assistant a private, token-free memory of your files — Claude, Gemini, Cursor, VS Code, Windsurf, Codex & Grok. Converts attachments (PDF/Office/EPUB/archives) to Markdown and builds a searchable knowledge-graph memory to recall from. 100% local, deterministic, model-free (no LLM/GPU). Incremental & portable; English + Bengali. MIT.
MTA_Plugin is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 44/100. Give any AI assistant a private, token-free memory of your files — Claude, Gemini, Cursor, VS Code, Windsurf, Codex & Grok. Converts attachments (PDF/Office/EPU. 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 MTA_Plugin to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/GRU953/MTA_Plugin.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": "GRU953/MTA_Plugin"
}Give any AI assistant a private, token-free memory of your files — Claude, Gemini, Cursor, VS Code, Windsurf, Codex & Grok. Converts attachments (PDF/Office/EPUB/archives) to Markdown and builds a searchable knowledge-graph memory to recall from. 100% local, deterministic, model-free (no LLM/GPU). Incremental & portable; English + Bengali. MIT.