WhatsApp Parser MCP for AI. Search Chat History. Find Lost Info Instantly.
Works with every AI agent you already use
…and any MCP-compatible client








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The WhatsApp Chat Export Parser MCP takes messy chat logs exported as `.txt` files and instantly converts them into clean, structured JSON data.
Your AI agent can then search years of conversation history, pull specific addresses, or summarize long threads without ever uploading your chats to the cloud.
What your AI can do
Parse whatsapp chat
Reads an exported WhatsApp chat text file offline, extracting every message, sender, timestamp, and participant count into structured JSON.
The agent searches the entire conversation history to pull out exact mentions, like finding one specific receipt or address.
It processes massive chat logs and delivers concise summaries of topics discussed over weeks or months.
The MCP calculates participant statistics, showing exactly who sent the most messages in a group chat.
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WhatsApp Chat Export Parser (1 Tool)
Use this single tool to parse exported WhatsApp text files. It extracts message content, timestamps, and participant statistics into structured JSON for your AI agent.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using WhatsApp Chat Export Parser on VinkiusParse Whatsapp Chat
Reads an exported WhatsApp chat text file offline, extracting every message, sender, timestamp, and participant count into structured JSON.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Finding critical details buried in years of conversation
Right now, finding something specific—say, the name of a restaurant or an agreement date—means digging through endless scrolls. You copy-paste chunks into notes apps and manually tag dates, making it tedious and error-prone.
With this MCP, you handle the raw chat export once. The agent processes that massive file, giving you clean JSON data. Now you can ask complex questions like, 'List every mention of a payment date,' and get an immediate, accurate list.
The parse_whatsapp_chat MCP delivers structured, searchable chat history.
You eliminate the manual steps: no more opening multiple tabs, no more cross-referencing dates across different message threads, and no more worrying about inconsistent date formats.
What's different now is that you treat your entire chat history not as a conversation, but as an infinitely searchable data source.
What your AI can actually do with this
Ever need an address your landlord sent you three months ago in a massive WhatsApp chat? Scrolling through thousands of messages is a nightmare. This MCP solves that common pain point. When you export any chat via WhatsApp's built-in feature, you get a raw .txt file—a mess to parse by hand.
This tool reads the entire log locally, extracting every message with its exact timestamp, who sent it, and what was said. It handles both US and EU date formats automatically. The resulting structured JSON lets your AI agent search, filter, and summarize your whole conversation history instantly, all while keeping your chats private on your machine.
Because this process runs within Vinkius's isolated sandbox, you keep absolute control of your data; nothing leaves your local environment during parsing. It’s a massive time-saver for anyone drowning in chat logs.
019e390a-1d39-7089-a587-24f7c4079219 Here's how it actually works
The bottom line is you take a useless wall of text and turn it into a structured database your AI can actually query.
First, you export your desired WhatsApp conversation using the built-in 'Export Chat' feature to get a raw .txt file.
You feed that local .txt file into the MCP. The tool then runs dual-locale regex parsing on the text to identify and structure every message.
The agent receives clean, searchable JSON data containing timestamps, senders, and content, ready for immediate analysis.
Who is this actually for?
Anyone who deals with large volumes of personal or professional chat history—from real estate agents to project managers. You're the person drowning in conversations, wasting hours manually hunting for that one critical detail.
Needs to quickly sift through years of client communication logs to find key dates or agreements mentioned casually over time.
Must analyze group chat threads with buyers to pinpoint which conversations contained specific property addresses or negotiation points.
Needs to summarize months of messy team chats to generate a status report showing who owned what task and when it was agreed upon.
What Changes When You Connect
Never manually comb through chat logs again. The parse_whatsapp_chat tool converts unstructured text into searchable JSON, so your agent can find specific details in seconds.
Keep all data private because the MCP runs locally. Your sensitive conversations never leave your machine or get uploaded to a cloud service.
It handles date formatting automatically. Whether the chat used US (1/15/23) or EU (15/01/2023) formats, it parses them correctly every time.
The agent can summarize huge volumes of text. You get high-level insights—like key decisions made or recurring topics—without reading thousands of messages.
You don't have to worry about context limits. The tool manages massive files by focusing on core data points, preventing your AI client from crashing due to chat log size.
See it in action
Finding a lost address
The agent needs to find the specific street address Maria mentioned months ago. You run parse_whatsapp_chat on the exported chat and ask the agent, 'Where did she say I live?' The result immediately gives you the exact date and message content.
Summarizing lease discussions
The landlord sent a 50-page chat history. You feed it to your agent and ask for a summary of 'renewal terms'. The MCP processes the data, and the agent returns a bulleted list covering all agreements.
Determining group activity
You want to know who dominated a large team chat. You use parse_whatsapp_chat on the logs, and your agent provides a clear breakdown: 'João sent 42% of messages.' The statistics are immediately available.
Extracting decision points
You need to pull every time payment dates were discussed. You run parse_whatsapp_chat, and the agent filters all records by topic, giving you a chronological list of agreement changes.
The honest tradeoffs
Treating chat logs like documents
Copy-pasting large sections of text into an AI prompt and asking it to summarize everything. This often fails because the sheer volume overwhelms the context window.
Use the parse_whatsapp_chat tool first. Run the raw .txt through the MCP; this structures the data into JSON, which your agent can then reliably read and summarize.
Searching chat logs by keyword only
Using basic search functions on a phone or computer to find 'address' in a massive history. This gives you dozens of irrelevant hits.
Use the MCP to parse the chat first, then ask your agent specific questions like, 'What is the full address mentioned?' The structured data ensures accuracy.
Relying on memory
Trying to remember if a decision was made last year or this month. This is unreliable and requires manual cross-referencing.
Feed the chat log through parse_whatsapp_chat. The resulting JSON structure locks down every message with an accurate timestamp, so your agent can pinpoint exactly when something happened.
When It Fits, When It Doesn't
Use this MCP if your goal is extracting structured data (addresses, dates, names) or summarizing content from raw chat exports. If you just need to read a few messages, don't bother; just scroll through the app. But if you have hundreds of pages of logs and need an AI agent to treat them like searchable records—that’s when this MCP is essential. Don't use it if your data lives in structured fields (like a CRM record); use that tool instead. This MCP is strictly for unstructured, exported chat text.
Questions you might have
Are my private chats sent to the cloud? +
Never. The parsing is 100% local. Only the structured text representation is sent to the AI chat context during your session.
How do I export a WhatsApp chat? +
Open the chat in WhatsApp, tap the three dots > More > Export Chat > Without Media. Save the .txt file to your computer.
What if the chat has 50,000 messages? +
The engine uses a token-safe strategy: it sends only the first 100 and last 50 messages, plus full participant stats. Ask the AI to filter by sender or date for specific results.
Does `parse_whatsapp_chat` handle different global date formats? +
Yes, it automatically handles multiple international date formats. The tool uses dual-locale regex parsing so you don't have to worry about US vs. European date conventions when exporting chats.
How does `parse_whatsapp_chat` process messages containing emojis or foreign languages? +
It processes them fully because the parsing happens entirely local. The engine captures raw text content, including complex Unicode characters, ensuring no language barrier affects data extraction.
Can `parse_whatsapp_chat` process multiple chat files at once? +
Currently, it processes one exported .txt file per call. If you have a batch of chats, you'll need to chain calls or write a simple script wrapper around the MCP tool.
What metadata does `parse_whatsapp_chat` capture for each entry? +
For every message, it captures three key pieces of information: the exact timestamp, who sent it (sender), and the full content. This structure allows your agent to perform highly specific searches.
Is the parsing done on a secure or private environment when using `parse_whatsapp_chat`? +
Yes, the entire process runs in a completely air-gapped sandbox. Your chats are parsed locally and never uploaded to any cloud service.
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