M

Mifosx

This project provides the Model Context Protocol (MCP) service for Apache Fineract®, supporting implementations in Python, Java, and Node.js, enabling AI agents to access financial data and operations. It includes a debugging tool, multi - language implementations, and standardized API interfaces.
2.5 points
20

What is the MCP server?

The MCP server is middleware that connects AI systems with the Apache Fineract financial platform, providing a secure API access channel through the standardized fineract:// protocol URI. It supports implementations in multiple programming languages and has built - in data validation and permission control.

How to use the MCP server?

Simply select any implementation version in Python, Java, or Node.js, configure the connection information of the Fineract instance, and you can start using it through the MCP Inspector tool or by directly calling the API.

Use cases

It is suitable for scenarios where AI capabilities need to be integrated into financial systems, such as fintech applications like intelligent customer analysis, automated loan approval, and risk prediction.

Main Features

Multi - language SupportProvides implementation versions in Python (Flask), Java (Quarkus), and Node.js to meet the needs of different technology stacks.
Standardized APIAccess financial data through the unified fineract:// resource identifier, shielding the differences of underlying APIs.
Developer ToolsComes with the MCP Inspector debugging tool, supporting two communication protocols: STDIO and SSE.
Core Financial ResourcesOut - of - the - box client/loan management interfaces, including operations such as querying, creating, and updating status.

Advantages and Limitations

Advantages
Unified access layer: Provides a consistent API interface for different Fineract versions.
Security isolation: AI agents do not need to directly access production environment credentials.
Plug - and - play: Presets common financial operation interfaces for quick integration of AI capabilities.
Limitations
Some functions in the Java version are inconsistent with other implementations (e.g., create_client is only supported in Node and Python).
Requires basic operation and maintenance capabilities for server deployment and maintenance.
Currently only supports basic financial entity operations, and complex business requires secondary development.

How to Use

Select an implementation version
Select the implementation in Python, Java, or Node.js according to your technology stack.
Configure environment variables
Set the BASE_URL, AUTH_TOKEN, and TENANT_ID of the Fineract instance.
Start the server
Run the startup command for the corresponding language (see the commands for each language below).
Connect to the debugging tool
Use the MCP Inspector for interface testing and debugging.

Usage Examples

Customer information queryThe AI system needs to obtain detailed information of a specific customer for credit evaluation.
Batch loan status checkThe risk monitoring system needs to regularly check overdue loans.
Intelligent customer searchThe customer service robot performs a fuzzy search for customer records based on the information provided by the user.

Frequently Asked Questions

Are the functions of different language versions completely consistent?
How to solve the environment variable prefix problem in the Java version?
Does it support the Windows system?
Deployment suggestions for the production environment

Related Resources

Apache Fineract official website
Official documentation for the open - source core banking system
MCP protocol specification
Technical standard documentation for the Model Context Protocol
GitHub repository
Project source code and issue tracking
Quarkus implementation guide
Detailed development documentation for the Java (Quarkus) version
Installation
Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.
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