Lunch Money for Agents

Ask your AI agent about your Lunch Money data.

Website · Setup guide · Privacy · Report an issue

I built this to make Lunch Money easier to use with AI agents. It connects your account to tools that support Model Context Protocol (MCP), so you can ask about your spending, budgets, accounts, and recurring expenses in conversation.

This is an unofficial, open-source MCP integration by n3wth. It is not affiliated with, endorsed by, or sponsored by Lunch Money.

What you can ask

• “Show my transactions from last week, grouped by category.”

• “How does this month's spending compare with my budget?”

• “List my recurring expenses.”

• “What are my account balances? Keep each currency separate.”

The integration gives your agent access to the underlying records; your agent uses those results to answer. Financial access is read-only: it cannot edit transactions, change budgets, or move money. Connection tools can link, replace, or remove your connection to the service.

Related MCP server: Lunch Money MCP Server

Connect your account

You'll need a Lunch Money account, a personal access token, and an MCP client that supports remote servers with OAuth sign-in.

1. Add the server using your client's setup instructions below.

2. Sign in at auth.n3wth.com. This creates your connector session, separate from your Lunch Money login.

3. Ask your agent to connect Lunch Money. It should run lunchmoney_connect and give you a browser link.

4. Open the link and enter your token in Nango's connection page. Create the token in Lunch Money under Settings → Developers. Never paste it into chat or your MCP configuration.

5. Try a small request, such as “List my Lunch Money accounts.”

The hosted endpoint is:

https://mcp.lunchmoney.sh/mcp

Client-specific installation

These links cover the configuration each client needs:

The hosted service is in beta. Direct setup is available; a directory submission does not mean a plugin is approved or listed. See the submission record for review status and the runbook for outstanding release checks.

Where your data goes

Signing in and connecting Lunch Money are separate steps:

The server retrieves your token from Nango when needed and enforces financial reads only. Your token may have broader permissions in Lunch Money itself. Financial responses are not saved in the connector's database, but your AI client's privacy and retention settings still apply to the results it receives.

To disconnect, ask your agent to run lunchmoney_disconnect. If deletion fails, retry until it succeeds. Revoke the original token in Lunch Money if you also want to prevent its use elsewhere. Removing a client plugin does neither.

Read the privacy notice for storage, retention, and provider details, and the service terms before connecting your account.

A couple of things to watch

Transactions arrive in pages. One response may not cover the whole date range. The agent needs to follow next_offset until it is null before presenting a complete total.

Currencies stay separate. Adding USD and CAD amounts together does not produce a useful total. Ask for separate totals unless you explicitly want a conversion.

AI-generated answers can be wrong. Check the source records in Lunch Money when an answer matters. The user guide lists the tools and explains connection troubleshooting.

Work on the project

Use Node.js 22 or later for server development. The site is plain HTML, CSS, and JavaScript with a dependency-free build.

git clone https://github.com/n3wth/lunchmoney-mcp.git
cd lunchmoney-mcp
npm run preview

This serves the site at http://localhost:4173. To build it without starting a server, run npm run build; the output goes into dist/.

Run the server checks in dependency order:

npm --prefix packages/auth-contract ci
npm --prefix packages/auth-contract test
npm --prefix packages/lunchmoney-adapter ci
npm --prefix packages/lunchmoney-adapter test
npm --prefix packages/server ci
npm --prefix packages/server test
node scripts/validate-release.mjs

Staging and production use separate configurations and databases. Default Worker commands target staging. Follow the runbook for deployment, credentials, migrations, and rollback.

Contribute

Bug reports, clearer setup instructions, and compatibility fixes are welcome. For a bug, include the client you used, what you expected, and a minimal example with sample data. Leave tokens and personal financial records out of issues.

Open an issue before a larger change so we can agree on the scope. For a pull request, explain the problem and how you checked the fix. Keeping financial access read-only is a core constraint of this project.