Local-first MCP tools for searching and drafting from your X/Twitter archive.
If you already use X Archive RAG, the x archive rag mcp server is the piece that lets your assistant work with it directly. Local-first MCP tools for searching and drafting from your X/Twitter archive.
Unlike uploading a ZIP to ChatGPT, x-archive-rag converts your archive into reusable local infrastructure: SQLite storage, repeatable retrieval, citation IDs, persona profiling, grounded draft prompts, a local web UI, and MCP tools that AI clients can query without receiving your whole archive.
x-archive-rag is not a bigger prompt. It is a small local data layer for your archive.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
search_archive — draft_from_archiveCapability — Why it mattersSetup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
Configuration is passed through the environment: OPENAI_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. X Archive RAG's toolset — search_archive, Capability — is a fair guide to whether it matches your workflow. It is maintained by mameshivaa; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| search_archive | draft_from_archive |
| Capability | Why it matters |
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.