Ragstack MCP: MCP server, setup and security

What Ragstack MCP does

According to its repository: “Access RAGStack serverless knowledge bases on AWS: search, chat with AI answers, upload documents and media, scrape websites and analyze metadata.”

An MCP server gives an AI agent extra tools it calls during the conversation: the agent reads their description, decides to use them and gets the result back. Ragstack MCP is listed under Memory and knowledge, a category of 250 servers in the directory, where it ranks #133 by stars.

Installing and connecting Ragstack MCP

Distribution modes declared in the official MCP registry:

• PyPI package (Python, started with uvx or pip)

Like any MCP server, you declare it in your client's configuration (Claude Desktop, Claude Code, Cursor, VS Code, Codex…): a name, the command that starts it or its URL, and any environment variables. The client then starts it and lists its tools. Project language: Python. Listed systems: macOS, Windows, Linux.

Security: what to check

An MCP server runs code with the rights you give it, and the model reads its tool descriptions: a malicious or compromised server can hijack the agent. Read the code, or pick a maintained and widely used server, before connecting it.

None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Ragstack MCP. That does not replace reading the code.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on October 26, 2025, the HatmanStack/RAGStack-Lambda repository has 25 stars and 10 forks, with 0 open issues. It has more stars than 42% of the servers in the directory. It received changes this month. Version published in the registry: 0.1.4 (February 6, 2026).

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.