Skip to main content
Glama
sharph

Lunch Money MCP Server

by sharph

Lunch Money MCP Server

A Model Context Protocol (MCP) server for the Lunch Money API v2, designed with minimal response sizes to prevent context window bloat.

Features

  • Optimized responses: Concise, formatted output to minimize token usage

  • Simple authentication: Uses environment variable for API token

  • Type-safe: Built with modern Python type hints

  • Easy to extend: Add more endpoints one at a time

Related MCP server: LunchMoney MCP Server

Currently Supported Endpoints

  • add_numbers - Helper tool for arithmetic operations

  • get_current_user - Get information about the authenticated user (GET /me)

  • get_transaction - Get details about a specific transaction by ID (GET /transactions/{id})

  • get_transactions - List transactions for a date range (GET /transactions)

Installation

  1. Clone this repository:

git clone <your-repo-url>
cd lunchmoney-mcp-mini
  1. Install dependencies using uv:

uv sync

Configuration

Get Your API Token

  1. Log in to Lunch Money

  2. Go to the Developers page

  3. Create a new API token or use an existing one

Set Environment Variable

export LUNCHMONEY_API_TOKEN="your-api-token-here"

Or create a .env file (not committed to git):

LUNCHMONEY_API_TOKEN=your-api-token-here

Usage

With Claude Desktop

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "lunchmoney-mini": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/lunchmoney-mcp-mini",
        "run",
        "lunchmoney_mcp_mini/main.py"
      ],
      "env": {
        "LUNCHMONEY_API_TOKEN": "your-api-token-here"
      }
    }
  }
}

Standalone Testing

# Make sure LUNCHMONEY_API_TOKEN is set
uv run lunchmoney_mcp_mini/main.py

Available Tools

add_numbers

Helper tool for performing arithmetic operations with precise decimal handling to avoid floating-point precision issues.

Parameters:

  • numbers (required): List of numbers to add together. Can include negative values for subtraction.

Returns:

  • sum: Sum rounded to 2 decimal places

  • input_count: Number of values provided

Example output:

{
  "sum": 123.45,
  "input_count": 3
}

get_current_user

Get details about the authenticated Lunch Money user.

Returns:

  • name: User's full name

  • email: User's email address

  • user_id: Unique user identifier

  • account_id: Unique account identifier

  • budget_name: Name of the budget

  • primary_currency: Primary currency code (e.g., 'usd')

  • api_key_label: Label for the API key being used

Example output:

{
  "name": "John Doe",
  "email": "john@example.com",
  "user_id": 12345,
  "account_id": 67890,
  "budget_name": "Family budget",
  "primary_currency": "usd",
  "api_key_label": "Development key"
}

get_transaction

Get full details about a specific transaction by its ID.

Parameters:

  • transaction_id (required): ID of the transaction to retrieve

Returns: Complete transaction object with all available fields including:

  • Core data: id, date, amount, currency, payee, original_name

  • Category/accounts: category_id, manual_account_id, plaid_account_id, recurring_id

  • Metadata: plaid_metadata, custom_metadata, files (if any)

  • Grouping/splitting: is_split_parent, split_parent_id, is_group_parent, group_parent_id, children

  • Timestamps: created_at, updated_at

  • Status: status, is_pending, source, external_id, tag_ids, notes

Example output:

{
  "id": 2112150655,
  "date": "2024-07-28",
  "amount": -45.50,
  "currency": "USD",
  "payee": "Whole Foods",
  "original_name": "WHOLE FOODS #1234",
  "category_id": 82,
  "status": "reviewed",
  "is_pending": false,
  "created_at": "2024-07-28T12:34:56.789Z",
  "updated_at": "2024-07-28T12:34:56.789Z"
}

get_transactions

List transactions within a specified date range.

Parameters:

  • start_date (required): Start date in YYYY-MM-DD format

  • end_date (optional): End date in YYYY-MM-DD format. Defaults to last day of start_date's month

  • category_id (optional): Filter by category ID

  • tag_id (optional): Filter by tag ID

  • status (optional): Filter by status ("reviewed", "unreviewed", "delete_pending")

  • is_pending (optional): Filter by pending status

  • manual_account_id (optional): Filter by manual account ID

  • plaid_account_id (optional): Filter by plaid account ID

  • recurring_id (optional): Filter by recurring item ID

  • include_pending (optional): Include pending transactions

  • limit (optional): Maximum number of transactions (1-2000, default 100)

  • offset (optional): Pagination offset

  • include_aggregates (optional): If True, calculates totals per category for full date range (respects all filters)

Returns:

  • transactions: Array of transaction objects

  • has_more: Boolean indicating if more transactions are available

  • aggregates (optional): Category totals and counts when include_aggregates=True

Transaction fields:

  • id: Transaction ID

  • date: Transaction date (YYYY-MM-DD)

  • amount: Transaction amount (numeric string)

  • payee: Payee name

  • category_id: Category ID

  • status: Transaction status

  • is_pending: Pending status

Aggregates fields (when include_aggregates=True):

  • by_category: Array sorted by total_amount descending, each with:

    • category_id: Category ID (or null for uncategorized)

    • category_name: Category name

    • count: Number of transactions in this category

    • total_amount: Sum of transaction amounts (numeric string)

  • total_count: Total number of transactions

  • total_amount: Sum of all transaction amounts (numeric string)

Example output (without aggregates):

{
  "transactions": [
    {
      "id": 2112150655,
      "date": "2024-07-28",
      "amount": "1250.8400",
      "payee": "Paycheck",
      "category_id": 88,
      "status": "reviewed",
      "is_pending": false
    }
  ],
  "has_more": false
}

Example output (with aggregates):

{
  "transactions": [...],
  "has_more": false,
  "aggregates": {
    "by_category": [
      {"category_id": 88, "category_name": "Rent", "count": 2, "total_amount": "2500.00"},
      {"category_id": 82, "category_name": "Groceries", "count": 5, "total_amount": "245.50"},
      {"category_id": null, "category_name": "Uncategorized", "count": 3, "total_amount": "45.00"}
    ],
    "total_count": 10,
    "total_amount": "2790.50"
  }
}

Design Philosophy

This MCP server is intentionally designed to return minimal, focused responses to avoid filling up the context window. Each tool:

  • Returns only essential information

  • Uses concise formatting

  • Avoids verbose JSON dumps

  • Provides human-readable output

Technical Details

This server uses:

  • FastMCP: A high-level Python framework for building MCP servers

  • requests-openapi: Automatically generates API client from OpenAPI spec

  • OpenAPI 3.0 spec: Ensures type safety and accurate API calls

The combination of FastMCP and requests-openapi means:

  • Less boilerplate code

  • Automatic request/response validation

  • Easy to add new endpoints from the spec

  • Type-safe API calls

Resources

License

MIT

F
license - not found
-
quality - not tested
D
maintenance

Maintenance

Maintainers
4dResponse time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sharph/lunchmoney-mcp-mini'

If you have feedback or need assistance with the MCP directory API, please join our Discord server