beanie-mcp

beanie-mcp

An MCP server for querying Beancount ledgers using natural language, enabling financial analysis through Claude with tools for BQL queries and ledger validation.

Category
Visit Server

README

beanie-mcp

An MCP server that lets AI agents inspect and query Beancount v3 ledgers with BQL. Point it at your .bean file, connect it to any MCP-capable client, and ask questions about your finances while the server handles ledger loading, validation, and structured query results.


What it does

Once connected, your agent can query your ledger directly without you touching a terminal. Under the hood, the run_query tool accepts BQL, not arbitrary natural language, so the agent translates your question into a valid query:

"What did I spend on restaurants last month?" "What's my current net worth across all accounts?" "Are there any errors or failed balance assertions in my ledger?" "Show me all transactions in my brokerage account this tax year."

beanie-mcp exposes four tools and three resources:

Name Type Description
run_query Tool Run a BQL query. Returns structured JSON with columns, stringified rows, returned row count, and truncation metadata. Invalid BQL or broken ledgers return an error field instead.
bean_check Tool Run bean-check on the ledger. Returns structured { ok, message, errors } JSON.
list_accounts Tool Return all declared accounts as structured JSON, without the query row cap.
list_tables Tool Return beanquery table names plus the key FROM caveat.
beanie://accounts Resource All accounts in the ledger, one per line.
beanie://tables Resource BQL-accessible table names.
beanie://bql-guide Resource Short BQL guide for agents, including caveats and examples.

BQL notes

BQL is SQL-like, but it is not SQL. The most important caveat: FROM is a date/filter clause, not a table selector. A query like this does not list accounts from the accounts table:

SELECT account FROM accounts ORDER BY account

For account discovery, use the list_accounts tool or beanie://accounts resource instead. For table discovery, use list_tables or beanie://tables.

Useful query examples:

SELECT account, sum(position)
WHERE account ~ "Expenses"
GROUP BY account
ORDER BY account
SELECT date, payee, narration, account, position
WHERE account ~ "Expenses:Food"
LIMIT 50
SELECT account, sum(position)
WHERE account ~ "Assets|Liabilities"
GROUP BY account

Result contract

run_query returns one of three shapes.

Successful query:

{
  "columns": ["account", "sum_position"],
  "rows": [["Expenses:Food", "123.45 USD"]],
  "truncated": false,
  "returned_rows": 1,
  "total_rows": 1,
  "total_rows_known": true
}

Broken ledger:

{
  "error": "Ledger has bean-check errors; fix them before querying.",
  "error_type": "ledger",
  "errors": [
    {
      "file": "/path/to/main.bean",
      "line": 42,
      "type": "BalanceError",
      "message": "Balance failed for ..."
    }
  ]
}

Invalid BQL:

{
  "error": "syntax error or beanquery error message",
  "error_type": "bql"
}

Rows are capped at 200. To keep broad queries from materialising an entire ledger, beanie-mcp fetches at most 201 rows. When truncated is true, total_rows is null and total_rows_known is false; add a narrower WHERE, ORDER BY, or LIMIT clause if you need a smaller answer. Row values are returned as strings so MCP clients get stable JSON even when beanquery returns Python dates, decimals, inventories, or other typed Beancount values.

bean_check returns:

{
  "ok": true,
  "message": "Ledger is clean - no errors or warnings.",
  "errors": []
}

or:

{
  "ok": false,
  "message": "Ledger has 1 error(s).",
  "errors": [
    {
      "file": "/path/to/main.bean",
      "line": 42,
      "type": "BalanceError",
      "message": "Balance failed for ..."
    }
  ]
}

Design goals

beanie-mcp is designed for real-world ledgers, not just small demos:

  • Structured JSON output - switched from BQLShell text tables to the beanquery DB-API (beanquery.connect()). Agents get columns and stringified rows they can actually work with, not a text table to parse.
  • Fail-loud ledger errors - run_query refuses to query ledgers with loader errors or failed balance assertions instead of returning plausible empty results.
  • Structured bean_check tool - surfaces loader errors and failed balance assertions as machine-readable JSON with file, line, type, and message.
  • Resource-safe row limit - query responses cap at 200 rows and only fetch one extra row to detect truncation.
  • Account tools bypass the cap - account enumeration fetches the full declared account list regardless of ledger size.
  • Locked ledger path - the ledger path is fixed by the BEANCOUNT_LEDGER env var at startup, so the agent cannot point the server at arbitrary files on your filesystem.
  • include-aware cache - the ledger is only re-parsed when the root file or any loaded include file changes, not on every query.
  • Watchdog auto-reload - file system watcher invalidates the cache when .bean files are modified, created, moved, or deleted.
  • Explicit pydantic-settings dependency - configuration loading does not rely on accidental transitive dependencies.
  • Python <3.14 ceiling - beancount 3.x has no prebuilt wheel for Python 3.14; building from source fails on macOS (Apple ships bison 2.3, beancount needs >=3.8). The ceiling prevents a confusing build failure.

Requirements

  • Python 3.10-3.13
  • uv
  • Beancount v3 ledger (.bean file)

Beancount v3 is the supported target. Beancount v2 ledgers may not work; if you are on v2, use a v2-compatible MCP server or query tool.

Install

Clone the repo and install the Python dependencies with uv:

git clone https://github.com/klinikal/beanie-mcp.git
cd beanie-mcp
uv sync

Find the absolute path to your main Beancount file. For example:

realpath ~/finance/main.bean

Use that full path as BEANCOUNT_LEDGER in the MCP config below. Relative paths are deliberately avoided because MCP clients may start the server from a different working directory.

You can smoke-test the server before adding it to an MCP client:

BEANCOUNT_LEDGER=/absolute/path/to/your/ledger/main.bean uv run beanie-mcp

The command starts an MCP stdio server and waits for a client. Press Ctrl+C to stop it.

Configure an MCP client

beanie-mcp is not tied to a particular model or agent. It is a standard local MCP stdio server. Any client that can launch a local MCP command with environment variables should be able to use it. That includes Claude Code/Desktop-style configs, Codex-style agent runners, Cursor-style IDE agents, Gemini-based agents, and other MCP-compatible tools. The exact config UI or file format depends on the client.

The generic command is:

{
  "command": "uv",
  "args": [
    "run",
    "--directory",
    "/absolute/path/to/beanie-mcp",
    "beanie-mcp"
  ],
  "env": {
    "BEANCOUNT_LEDGER": "/absolute/path/to/your/ledger/main.bean"
  }
}

Some clients wrap that command in an mcpServers object. Others have a GUI where you enter the same command, args, and env vars separately.

Example: Claude Code / Claude Desktop

For clients that use an mcpServers JSON block, add:

{
  "mcpServers": {
    "beanie": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/absolute/path/to/beanie-mcp",
        "beanie-mcp"
      ],
      "env": {
        "BEANCOUNT_LEDGER": "/absolute/path/to/your/ledger/main.bean"
      }
    }
  }
}

Example: Codex

Add a server entry to your Codex config:

[mcp_servers.beanie]
command = "uv"
args = [
  "run",
  "--directory",
  "/absolute/path/to/beanie-mcp",
  "beanie-mcp",
]

[mcp_servers.beanie.env]
BEANCOUNT_LEDGER = "/absolute/path/to/your/ledger/main.bean"

Other MCP clients

Use the same command, args, and env vars wherever your client defines local MCP servers:

{
  "command": "uv",
  "args": [
    "run",
    "--directory",
    "/absolute/path/to/beanie-mcp",
    "beanie-mcp"
  ],
  "env": {
    "BEANCOUNT_LEDGER": "/absolute/path/to/your/ledger/main.bean"
  }
}

Restart the client after changing MCP config. MCP clients usually read the tool list only when they start.

Verify

Once connected, ask your agent to run:

  • bean_check
  • list_accounts
  • list_tables

Then try a small BQL query:

SELECT account, sum(position)
WHERE account ~ "Expenses"
GROUP BY account
LIMIT 20

If bean_check reports errors, fix the ledger first. run_query refuses to query a broken ledger so the agent does not mistake an invalid ledger for an empty result.

Update

To update an existing local checkout:

cd /absolute/path/to/beanie-mcp
git pull
uv sync

Restart your MCP client after updating.

Troubleshooting

The client cannot find uv
Use the absolute path to uv in your config. Find it with:

which uv

Then replace "command": "uv" with something like "command": "/opt/homebrew/bin/uv".

No ledger configured
Set BEANCOUNT_LEDGER in the MCP config env block. It must point to your main .bean file.

Ledger file not found
Use absolute paths for both /absolute/path/to/beanie-mcp and BEANCOUNT_LEDGER. ~ may not expand inside every MCP client.

Tool list did not change after updating
Restart the client. Long-running MCP clients often keep the old tool list until they reconnect.

Development

Run the MCP inspector:

BEANCOUNT_LEDGER=/absolute/path/to/your/ledger/main.bean uv run mcp dev src/beanie_mcp/server.py

Running tests

uv run pytest server_test.py -v
uv run ruff check .
uv build

Privacy

This tool sends parts of your Beancount ledger to whatever model/provider your MCP client uses. Only connect it to a provider and client you trust with your financial data. The relevant data handling policy is the one for the model/provider/client you choose.

You are responsible for your financial data. Don't connect this to a service you wouldn't trust with your bank statements. Run this at your own risk.

License

MIT. See LICENSE.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured