mcp-fin-ledger-interface: Ledger CLI Model Context Server

Synopsis

This Model Context Protocol (MCP) server facilitates interaction with Ledger CLI, a robust, command-line driven double-entry bookkeeping application. This server bridges the gap, allowing sophisticated Large Language Models (LLMs) to securely interrogate and derive insights from your proprietary financial dataset managed by Ledger.

The core function of this service is to expose the capabilities of Ledger CLI through a standardized API schema, thereby simplifying routine yet complex AI-assisted financial management tasks, including performance reporting, expense allocation analysis, and general ledger reconciliation.

Exposed Functionality

The server furnishes a set of distinct functions mapped directly to established Ledger CLI subcommands:

1. ledger_balance
2. Retrieves current monetary positions across accounts, supporting advanced data filtering.
3. Parameters: RegEx for account matching, time bounds, output formatting selections.

4. Output: A structured summary of account balances.

5. ledger_register

6. Fetches a chronological ledger of transactions.
7. Parameters: Transaction query pattern, temporal scope, ordering criteria.

8. Output: A formatted record of transaction history.

9. ledger_accounts

10. Enumerates all defined accounting entities within the ledger file.
11. Parameter: Optional pattern for filtering account names.

12. Output: A list containing matching account identifiers.

13. ledger_payees

14. Generates a list of all entities recorded as payees.
15. Parameter: Optional pattern for payee name matching.

16. Output: A collection of identified payees.

17. ledger_commodities

18. Lists all distinct monetary units or assets tracked.
19. Parameter: Optional commodity symbol pattern.

20. Output: A catalog of tracked commodities.

21. ledger_print

22. Outputs raw transaction data in the native Ledger file format.
23. Parameters: Selection criteria (query), date parameters.

24. Output: Text output containing matching ledger entries.

25. ledger_stats

26. Computes and presents aggregate statistics about the ledger's contents.
27. Parameter: Optional pattern for focusing statistics.

28. Output: A statistical overview.

29. ledger_budget

30. Executes a comparison between actual performance and predefined budget targets.
31. Parameters: Budget query, date constraints, periodicity setting.

32. Output: A detailed budget adherence report.

33. ledger_raw_command

34. Permits execution of any arbitrary, unmapped Ledger CLI operation.
35. Parameter: A list representing the command arguments to be passed.
36. Output: The standard output stream from the executed command.

Dependencies

• Ledger CLI: Must be present in the system's executable search path.
• Data File: A properly structured Ledger data file is required to operate.

Deployment

Containerized Method (Recommended)

Utilize the pre-built Docker image available from the repository:

bash docker pull minhyeoky/mcp-ledger

Configuration snippet for claude_desktop_config.json:

"mcp-ledger": { "command": "docker", "args": [ "run", "-v", "/path/to/your/ledger/file.ledger:/main.ledger", "-e", "LEDGER_FILE=/main.ledger", "-i", "--rm", "minhyeoky/mcp-ledger" ] }

Remember to substitute the placeholder volume mount path.

Smithery Installation

Automated setup for Claude Desktop via the Smithery package manager:

bash npx -y @smithery/cli install @minhyeoky/mcp-ledger --client claude

Local Execution with uv

The most straightforward approach for local deployment involves using the uv package manager:

bash uv sync

Configuration Details

The server's operational context hinges on locating the primary Ledger data file. This path is established via one of two mechanisms:

• Setting the LEDGER_FILE environment variable.
• Passing the file path as a runtime argument during server instantiation.

Integration Example (Claude Desktop)

If running via a local Python environment managed by uv, the configuration might resemble this in claude_desktop_config.json:

"mcpServers": { "ledger": { "command": "uv", "args": [ "run", "--env-file", "/path/to/your/.env", // Optional: For environment variable injection "--with", "mcp[cli]", "mcp", "run", "/main.py" // Entry point script location ] }

}

Operational Prompts

Upon successful integration, the AI assistant can handle queries such as:

• "Provide an itemized breakdown of disbursements from the preceding thirty-day period."
• "What is the aggregated holding across all asset accounts currently?"
• "List every recorded entry associated with the vendor 'Amazon'."
• "Quantify total expenditure designated for 'groceries' throughout the calendar year 2023."
• "Assess the adherence to the Q1 budgetary allocations."

The assistant automatically selects and invokes the correct Ledger CLI function through this server interface.

Debugging Utilities

For interactive, local diagnostic execution:

bash mcp dev main.py

Development Guidelines

This infrastructure is constructed using the Model Context Protocol Python SDK. Refer to the official documentation for comprehensive details on MCP architecture and server development.

To contribute enhancements:

1. Clone the source repository.
2. Resolve development dependencies.
3. Implement and refine features.
4. Validate changes using the MCP inspector or via integration testing within Claude Desktop.

Security Protocol

Execution of Ledger CLI operations involves processing sensitive financial documentation. Although the server incorporates safeguards against direct command injection, users must adhere to best security practices:

• Restrict access to trusted conversational clients only.
• Exercise prudence regarding the filesystem paths exposed to the server.
• Periodically audit the commands being processed.

Licensing

This MCP service is distributed under the permissive MIT License. Modification and redistribution are permitted according to its terms.

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