Pane – Give your AI access to your financial data via MCP
Hey HN! Today I'm launching Pane, a tool that gives AI context on your financial data. Once connected, any MCP-compatible client (Claude, Cursor, ChatGPT, etc.) can answer questions like: - "What did I spend on food this month?" - "What's my net worth right now?" - "Show me my recurring subscriptions" - "How much do I owe across all credit cards?" - "What are my investment holdings?" It's been really transformative in helping me and some of my friends understand their finances, where they are overspending, or even being double billed in some cases. I'm very aware that this is a somewhat…
Pane – Give your AI access to your financial data via MCP launched on March 3, 2026 with 10 votes, #1,177 of 23,380 launches that month and more than 90% of that year's launches.
In plain words
Pane is a tool that connects personal financial data to AI assistants through the MCP protocol, allowing users to query information across multiple accounts. It works with Claude, Cursor, ChatGPT, and other compatible clients to answer questions about spending patterns, net worth, subscriptions, debts, and investments. The tool aims to help users identify overspending and billing issues by giving AI systems access to their banking and financial information.
written from the facts on this page · September 2026
From the sources
Chat with your finances across Claude, ChatGPT and more. Starting at $15/month.
Chat with your finances across Claude, ChatGPT and more. Starting at $5/month.
Link your accounts. Give AI privacy-authorized context to find accounts and transactions. Connect through Plaid. Your credentials never touch our servers. You control your data. Works with Claude, ChatGPT, OpenClaw, or any MCP-compatible tool — including local AI. Ask about spending, balances, and trends. Pane keeps your data up tofrom pane.money
In the maker’s words, at launch
Hey HN! Today I'm launching Pane, a tool that gives AI context on your financial data. Once connected, any MCP-compatible client (Claude, Cursor, ChatGPT, etc.) can answer questions like: - "What did I spend on food this month?" - "What's my net worth right now?" - "Show me my recurring subscriptions" - "How much do…
Does a similar job
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Alpha Vantage MCPNov 2025 · mcp.alphavantage.co · ▲104We just released an MCP server that lets Claude (or any MCP client) pull real-time and historical financial data: equities, options chains, crypto, FX, commodities, fundamentals, economic indicators, and 50+ technical signals. You can ask things like: “Plot NVDA’s RSI vs AMD’s over the past year.” “Show the yield curve shift since 2020.” “What are Apple’s recent earnings surprise trends? “Make a 3-statement model for ORCL.” Claude runs the requests, does the math, and returns the data or generates charts/models all inside the chat. Setup takes about a minute. You’ll need Claude Pro…
LangAlpha – what if Claude Code was built for Wall Street?Apr 2026 · github.com · ▲148Some technical context on what we ran into building this. MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80…
Forget Rigid Stock Screeners – A Universal Query API for Financial DataSep 2026 · financialdata.net · ▲10Connect Your AI to Institutional-Grade Market Intelligence Plug any AI client, from ChatGPT to custom agents, directly into our financial data engine. Get real-time stock prices, fundamentals, institutional trading insights, and other financial data delivered through a universal Model Context Protocol (MCP) server.
I built an AI agent that helps me invest2025 · github.com · ▲32A while back, I built a simple app to track stocks. It pulled market data and generated daily reports based on my risk tolerance. Basically a personal investment assistant. It worked well enough that I kept going. Now, the same framework helps me with real estate: comparing neighborhoods, checking flood risk, weather patterns, school zones, old vs. new builds, etc. It’s a messy, multi-variable decision—which turns out to be a great use case for AI agents. Instead of ChatGPT or Grok 4, I use mcp-agent, which lets me build a persistent, multi-agent system that pulls live data, remembers my…
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