Mifos X MCP Server for Fineract

What does “Mifos X” MCP Server do?

The Mifos X MCP Server acts as a bridge between AI assistants and the Apache Fineract® API, making financial data and operations accessible to AI-driven workflows. By implementing the Model Context Protocol (MCP), it enables AI agents to interact with core banking resources—such as clients and loans—through a standardized interface. The server supports multiple languages (Python, Java/Quarkus, Node.js) and provides tools for searching clients, creating new clients, and updating loan statuses. This MCP server enhances developer productivity by streamlining integration with Fineract, automating repetitive tasks, and enabling advanced AI-powered financial data processing and workflow automation.

List of Prompts

No prompt templates are mentioned in the repository.

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List of Resources

• fineract://clients
  List all clients available in the Fineract system.
• fineract://clients/{clientId}
  Retrieve details for a specific client by their client ID.
• fineract://loans
  List all loans in the Fineract database.
• fineract://loans/{loanId}
  Get details for a specific loan using its loan ID.

List of Tools

• search_clients
  Search for clients by name or attributes.
• create_client
  Create a new client in Fineract (available in Node.js and Python).
• update_loan_status
  Update the status of a loan (available in Java and Python).

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Use Cases of this MCP Server

• Automated Client Search:
  AI agents can rapidly search and retrieve client information, streamlining KYC (Know Your Customer) processes for financial institutions.
• Onboarding New Clients:
  Automate client creation directly from conversational AI workflows, reducing manual data entry and improving onboarding efficiency.
• Loan Portfolio Management:
  Enable AI to list, filter, and retrieve detailed information about loans, supporting risk assessment and customer support scenarios.
• Loan Status Updates:
  Facilitate automated updates to loan status (e.g., approval, disbursement) from within AI-powered process automation tools.
• Financial Data Aggregation:
  Aggregate client and loan data for analytics, reporting, or AI-driven decision-making without direct human intervention.

How to set it up

Windsurf

1. Ensure you have Node.js, Python, or Java (as required) installed.
2. Install the Mifos X MCP Server via the chosen language (see “Getting Started” in the repo).
3. Locate your Windsurf configuration file (e.g., windsurf.json or similar).
4. Add the Mifos X MCP Server in the mcpServers section:

   {
     "mcpServers": {
       "mifosx": {
         "command": "python",
         "args": ["app.py"]
       }
     }
   }
   

5. Save and restart Windsurf, then verify the server is available.

Securing API Keys Example:

{
  "env": {
    "FINERACT_BASE_URL": "https://your-fineract-instance",
    "FINERACT_BASIC_AUTH_TOKEN": "your_api_token",
    "FINERACT_TENANT_ID": "default"
  },
  "inputs": {}
}

Claude

1. Install Node.js, Python, or Java as needed.
2. Deploy the Mifos X MCP Server implementation of your choice.
3. Open the Claude agent configuration file.
4. Add the MCP server:

   {
     "mcpServers": {
       "mifosx": {
         "command": "node",
         "args": ["server.js"]
       }
     }
   }
   

5. Save changes and restart Claude, verifying the integration.

Securing API Keys Example:

{
  "env": {
    "FINERACT_BASE_URL": "https://your-fineract-instance",
    "FINERACT_BASIC_AUTH_TOKEN": "your_api_token",
    "FINERACT_TENANT_ID": "default"
  },
  "inputs": {}
}

Cursor

1. Install prerequisites for Python, Node.js, or Java.
2. Set up the MCP server using the preferred language.
3. Edit Cursor’s settings to include the MCP server:

   {
     "mcpServers": {
       "mifosx": {
         "command": "python",
         "args": ["app.py"]
       }
     }
   }
   

4. Restart Cursor and ensure the server connection is active.

Securing API Keys Example:

{
  "env": {
    "FINERACT_BASE_URL": "https://your-fineract-instance",
    "FINERACT_BASIC_AUTH_TOKEN": "your_api_token",
    "FINERACT_TENANT_ID": "default"
  },
  "inputs": {}
}

Cline

1. Install dependencies (Node.js, Python, or Java).
2. Run the Mifos X MCP Server as per instructions.
3. Add the server to Cline’s mcpServers configuration:

   {
     "mcpServers": {
       "mifosx": {
         "command": "java",
         "args": ["-jar", "mcp-server.jar"]
       }
     }
   }
   

4. Save and restart Cline, verifying server accessibility.

Securing API Keys Example:

{
  "env": {
    "MIFOSX_BASE_URL": "https://your-fineract-instance",
    "MIFOSX_BASIC_AUTH_TOKEN": "your_api_token",
    "MIFOS_TENANT_ID": "default"
  },
  "inputs": {}
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "mifosx": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “mifosx” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the available information and repository completeness, this MCP server offers solid financial resource and tool coverage, but lacks prompt templates and sampling support. The documentation is clear and setup is well described. Overall, a reliable MCP for Fineract automation but with room for improvement in advanced features.

MCP Score