Connect a local WhatsApp archive to Claude through a narrowly scoped MCP, with explicit participant privacy, retention, tool permissions, and read only defaults so message search does not quietly become unrestricted surveillance or sending.
Source episode · 29:47
How I AI: 3 AI Workflows to Save $140K & Automate Marketing with ElevenLabs' Luke Harrieswith Claire Vo
Luke runs an unofficial local WhatsApp bridge, authenticates it through the WhatsApp Web QR flow, stores synchronized messages in SQLite, exposes search and send capabilities through an MCP server, and connects Claude to summarize recent groups and search messages about a topic.
A local searchable message index with bounded MCP tools, clear sync status, source chat and timestamp citations, and sending disabled or tightly constrained until the owner intentionally enables it.
Anthropic AI assistant
Step by step
Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.
5 steps
Clone and inspect the bridge and MCP code, dependencies, permissions, storage paths, and exposed tools. Run it under a dedicated local user or directory, keep secrets and databases out of Git, and start with send disabled.
Authenticate through the WhatsApp Web QR flow on the owner's device. Confirm which chats and history sync, where SQLite and media are stored, how updates run, and how to revoke the linked session and delete local data.
Run the MCP server locally with read only tools first. Add authentication where supported, bind only to the intended interface, minimize logs, and restrict chat IDs, time range, result size, and attachment access.
Register the local server with Claude and inspect every discovered tool and schema. Disable or wrap broad send, delete, and attachment actions before granting message search access.
Query one allowed chat and short time range, requiring citations and explicit coverage. Expand only after confirming privacy and accuracy; draft outbound messages separately and send only through a deliberate constrained action.
Search only [allowed chat] from [start] to [end] for [topic]. Return a concise summary with source message IDs or timestamps, participants as permitted, disagreements, and missing coverage. Do not search other chats, expose unrelated content, or send any message.
Turn an idea into a PRD, user stories, and a plan.
After the steps
How to recover when the loop fails and where human judgment helps.
Recover
Keep building
Create context-aware guardrails for your enterprise AI agent using a 'Project' governance layer. As a project owner, define rules, scope tools, and require human-in-the-loop approval for sensitive actions, ensuring safe and effective AI use.
Transform a successful AI chat session into a reusable skill that anyone in your company can use. Package prompts, tools, and code into a standardized workflow, making it easy to share and scale automation across your organization.
Use a natural language AI agent to instantly generate and iterate on custom data dashboards. This workflow queries your company's data securely, allowing non-technical users to perform their own 'last mile' data analysis without filing tickets.
Join 100,000+ product managers who use ChatPRD to write better docs, align teams faster, and build products users love.