MCP server for AI: retrieve and save Wayback Machine…

This automation template transforms the Wayback Machine API into an accessible tool for AI agents via the MCP interface. It allows agents to request and save web page snapshots in the Internet Archive without manual API handling, simplifying integration of historical web data.

## Who it´s for
- AI agent developers integrating access to archived web pages
- Researchers automating data collection from historical website versions
- Legal professionals and companies needing content restoration by URL or proof of page existence on a specific date

## What the automation does
- Receives HTTP requests from AI agents via webhook as MCP tool calls
- Translates requests into API calls to https://api.archive.org with automatic parameter binding using $fromAI()
- Supports two operations: retrieving the latest archived version of a page and creating a new snapshot
- Returns structured JSON responses compatible with the original Wayback Machine API format
- Provides transparent error and status pass-through from the API

## What´s included
- Ready-to-use n8n workflow
- Logic for handling two request types: retrieve and create snapshot
- Integrations with Internet Archive API and external AI agents via HTTP
- Basic textual instructions for launch and adaptation

## Requirements for setup
- n8n instance (self-hosted or cloud)
- Access to LangChain MCP Server if used in stack
- Network access to https://api.archive.org
- Familiarity with n8n and HTTP API basics

## Benefits and outcomes
- Simplifies AI agent integration with archival data
- Automates retrieval and archiving of web snapshots without manual input
- Enables use of historical web data in AI reasoning and decision-making
- Lowers entry barrier for using Wayback Machine within autonomous agents

## Important: template only
Important: you are purchasing a ready-made automation workflow template only. Rollout into your infrastructure, connecting specific accounts and services, 1:1 setup help, custom adjustments for non-standard stacks and any consulting support are provided as a separate paid service at an individual rate. To discuss custom work or 1:1 help, contact via Telegram: @gleb923.