Financial Ledger Query Agent (MCP)

This Beancount MCP Server component serves as an initial, proof-of-concept implementation adhering to the Model Context Protocol (MCP). It is specifically engineered to interact with plaintext data residing in Beancount ledger documents. By harnessing the power of the Beancount Query Language (BQL) via the underlying beanquery utility, this service enables fluid querying and intricate financial analytics on data structured in the Beancount format. Integrating with the MCP standard allows for unified, predictable communication channels between generative AI agents and personal accounting ledgers, dramatically boosting the actionable value derived from the stored financial history.

Caveat: Deployment of this specific server instance is considered experimental; expect potential interface modifications. User feedback is highly encouraged to guide future stabilization and feature iteration.

Reference material for generating sample data: sample.bean

Accessible Capabilities and Data Sources

• Operational Functions (Tools):
• set_ledger_file: Designate the primary Beancount ledger file for subsequent query operations (if not pre-configured via environment parameters).
• run_query: Execute a specified BQL statement against the presently loaded Beancount dataset.
• Data Contexts (Resources):
• beanquery://tables: Retrieve the schema inventory detailing accessible relational views provided by BQL.
• beanquery://accounts: Obtain a comprehensive enumeration of all financial accounts indexed within the active ledger file.

Demonstration with Claude.ai

For a visual representation detailing the extended MCP interaction flow, consult here.

⚠️ Critical Data Security Notice

This utility mediates access to your ledger data via external language model (LLM) infrastructure through the MCP framework. Consequently, segments of your Beancount contents—potentially encompassing sensitive proprietary or personal monetary figures—might be transmitted to external, third-party processing services.

Exercise rigorous caution when employing this component, particularly under the following conditions: - Your ledger includes high-sensitivity elements (e.g., client billing rates, compensation data, private expense classifications). - Your operational backend relies on a publicly accessible or cloud-based LLM inference engine.

Mitigation Strategies: - Always scrub or anonymize sensitive fields within test or shared ledger files. - Prioritize the utilization of locally hosted or self-managed LLM environments whenever feasible. - Regularly audit the data payload transmitted over MCP to confirm adherence to all established privacy and security protocols.

[!CAUTION] The ultimate accountability for safeguarding your financial documentation rests solely with the operator. Refrain from sharing ledger contents that you would be hesitant to publicly disclose.

Installation Roadmap

Prerequisites

• Runtime environment must support Python version 3.10 or newer.
• Utilization of the uv dependency manager is highly recommended for streamlined project setup.

Activating the Server

1. Debugging/Development Workflow: Invoke the server for interactive inspection and debugging using the MCP Inspector utility: bash mcp dev server.py

2. Integration with Claude Desktop: Install the service endpoint directly into the Claude Desktop application environment: bash mcp install server.py

3. Rapid Deployment Scenario: bash uv run mcp install server.py -v BEANCOUNT_LEDGER=$(pwd)/sample.bean --with beancount --with beanquery

4. Custom Naming Convention: bash uv run mcp install server.py --name "Beancount Financial Data Interface" --with beancount --with beanquery

5. Configuration via Variables/Files: bash uv run mcp install server.py -v BEANCOUNT_LEDGER=/abs/path/to/your/financials.bean --with beancount --with beanquery uv run mcp install server.py -f .env --with beancount --with beanquery

Validation

Execute the comprehensive test suite using the pytest framework: bash pytest server_test.py

Participation Guidelines

1. Create a personal fork of the repository.

2. Establish a dedicated feature branch for your contributions: bash git checkout -b enhancement-description

3. Commit your introduced modifications: bash git commit -m "Implement description of new functionality"

4. Synchronize your branch to the remote repository: bash git push origin enhancement-description

5. Submit a formal Pull Request for review.

Software Licensing

This project is distributed under the terms specified in the MIT License. Detailed terms are available in the accompanying LICENSE file.

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