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MCP server · AI

Beanquery MCP

Beancount MCP Server is an experimental implementation that utilizes the Model Context Protocol (MCP) to enable AI assistants to query and analyze Beancount ledger files using Beancount Query Language (BQL) and the beanquery tool.

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Scored 3 months ago · breakdown

About Beanquery MCP

Beanquery MCP is an MCP server published by vanto in the AI category: beancount MCP Server is an experimental implementation that utilizes the Model Context Protocol (MCP) to enable AI assistants to query and analyze Beancount ledger files using Beancount Query Language (BQL) and the beanquery tool. It has been installed 0 times through Conduid.

The repository has 41 stars and 3 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.

Install

Install
npx beanquery-mcp

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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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.

README mirrored from the source repository 3 months ago. The original is authoritative.

Questions

About Beanquery MCP

How do I install Beanquery MCP?

Run npx beanquery-mcp, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is Beanquery MCP safe to use with an AI agent?

Its trust score is 62 out of 100 (good). It passes 0 of 1 static security checks; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Beanquery MCP still maintained?

The last commit was a year ago, with 0 open issues. That's long enough that you should check whether the maintainer is responding to issues before depending on it.