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Beanquery MCP

Beanquery MCP

Local
github.com/vantoFinance53Updated 82w ago

About Beanquery MCP

Beanquery MCP is an experimental MCP server that lets AI assistants query and analyze Beancount ledger files using BQL and the beanquery tool. It targets users of plaintext accounting who want standardized access to their financial data. The README warns that ledger contents may be sent to LLM providers.

Key features

  1. 01Runs BQL queries against a loaded ledger
  2. 02Lists accounts from the ledger file
  3. 03Lists tables accessible to BQL
  4. 04Sets the ledger file via environment variable or tool

Use cases

  1. 01Letting an AI assistant query personal ledger transactions
  2. 02Analyzing account data in a Beancount ledger
  3. 03Testing ledger query workflows in a development environment

MCP support

This is an MCP server that exposes the run_query and set_ledger_file tools, plus the beanquery://tables and beanquery://accounts resources, to MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Beanquery MCP

The Beancount MCP Server is an experimental implementation of the Model Context Protocol (MCP) designed to interface with Beancount ledger files. Leveraging the Beancount Query Language (BQL) and the beanquery tool, this server enables seamless querying and analysis of financial data stored in Beancount format. By integrating MCP, the server facilitates standardized communication between AI assistants and Beancount ledgers, enhancing the accessibility and utility of financial data.

Note: This server is experimental and may undergo significant changes. It is recommended to use it in a development environment and provide feedback for further improvements.

A generated sample ledger can be found in sample.bean

Available Resources and Tools
  • Tools:
    • set_ledger_file: Set the Beancount ledger file to use for queries (if not set via environment variable).
    • run_query: Run a BQL query against the loaded Beancount file.
  • Resources:
    • beanquery://tables: Get a list of tables that BQL can access.
    • beanquery://accounts: Get a list of accounts in the loaded Beancount file.

Example using Claude.ai

screenshot

For a screenshot with expanded MCP interactions, see here.

⚠️ Privacy Warning

This tool interfaces with language model providers (LLMs) via the Model Context Protocol (MCP), and as such may transmit parts of your Beancount ledger—including potentially confidential or private financial information—to third-party services.

Please use this tool with caution, especially when:

  • Your ledger contains sensitive data (e.g., client names, salary details, health-related expenses).
  • You are using a cloud-hosted or non-self-hosted LLM backend.

Recommendations:

  • Mask or redact sensitive data in test ledgers.
  • Use self-hosted LLMs when possible.
  • Review the data being sent via MCP to ensure compliance with your privacy and security requirements.

Caution

You are responsible for the protection of your financial data. Do not share ledgers you would not be comfortable exposing.

Setup

Prerequisites
  • Python 3.10 or later
  • uv for managing Python projects (recommended)

Usage

Running the Server
  1. Development Mode: Use the MCP Inspector to test and debug your server:

    mcp dev server.py
    
  2. Claude Desktop Integration: Install the server into Claude Desktop:

    mcp install server.py
    
    • Quick Start:

      uv run mcp install server.py -v BEANCOUNT_LEDGER=$(pwd)/sample.bean --with beancount --with beanquery
      
    • Custom Name:

      uv run mcp install server.py --name "Beanquery MCP Server" --with beancount --with beanquery
      
    • Environment Variables:

      uv run mcp install server.py -v BEANCOUNT_LEDGER=/path/to/your/ledger.bean --with beancount --with beanquery
      uv run mcp install server.py -f .env --with beancount --with beanquery
      

Testing

Run the test suite using pytest:

pytest server_test.py

Contributing

  1. Fork the repository.
  2. Create a feature branch:
    git checkout -b feature-name
    
  3. Commit your changes:
    git commit -m "Add feature description"
    
  4. Push to your branch:
    git push origin feature-name
    
  5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.