What are the prerequisites for Beanquery MCP?
Python 3.10 or later and uv (recommended) for managing the project.
@vanto
Basic information
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.
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"beanquery-mcp": {
"command": "uv",
"args": [
"run",
"mcp",
"install",
"server.py",
"-v",
"BEANCOUNT_LEDGER=$(pwd)/sample.bean",
"--with",
"beancount",
"--with",
"beanquery"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Beanquery MCP is an experimental Model Context Protocol (MCP) server that interfaces with Beancount ledger files. It uses the Beancount Query Language (BQL) and the beanquery tool to enable querying and analysis of financial data, allowing AI assistants to interact with Beancount ledgers through a standardized protocol.
Install with Python 3.10+ and uv (recommended). Run in development mode using mcp dev server.py. Install into Claude Desktop with mcp install server.py, optionally setting the BEANCOUNT_LEDGER environment variable. Use the run_query tool to execute BQL queries and the set_ledger_file tool to specify a ledger file. Access resources at beanquery://tables and beanquery://accounts.
Python 3.10 or later and uv (recommended) for managing the project.
Use the `set_ledger_file` tool or set the `BEANCOUNT_LEDGER` environment variable when installing.
It provides `run_query` to run BQL queries and `set_ledger_file` to specify the ledger file.
No, the server is experimental and may undergo significant changes. Use in a development environment.
Yes, parts of your ledger may be transmitted to LLM providers via MCP. The README includes a privacy warning and recommends caution, especially with sensitive data.

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