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Built with ❤️ by Krishna Goyal

    Darajamcp

    14 stars
    Python
    Updated Sep 2, 2025

    Table of Contents

    • What is an MCP Server?
    • Overview
    • Key Capabilities
    • Requirements
    • Installation
    • Step 1: Setting Up Your Environment
    • Step 2: Setting up Environment Variables
    • Usage
    • Testing with Claude Desktop
    • Tools and Prompts
    • Payment Tools
    • stk_push
    • generate_qr_code
    • Payment Prompts
    • stk_push_prompt
    • generate_qr_code_prompt
    • Document Processing Tools
    • create_source
    • create_destination
    • create_workflow
    • run_workflow
    • get_workflow_details
    • fetch_documents
    • Prompts
    • create_and_run_workflow_prompt
    • Resources
    • License
    • Acknowledgments
    • Contact

    Table of Contents

    • What is an MCP Server?
    • Overview
    • Key Capabilities
    • Requirements
    • Installation
    • Step 1: Setting Up Your Environment
    • Step 2: Setting up Environment Variables
    • Usage
    • Testing with Claude Desktop
    • Tools and Prompts
    • Payment Tools
    • stk_push
    • generate_qr_code
    • Payment Prompts
    • stk_push_prompt
    • generate_qr_code_prompt
    • Document Processing Tools
    • create_source
    • create_destination
    • create_workflow
    • run_workflow
    • get_workflow_details
    • fetch_documents
    • Prompts
    • create_and_run_workflow_prompt
    • Resources
    • License
    • Acknowledgments
    • Contact

    Documentation

    Daraja MCP

    🚨 Important Notice: Repository Moved

    This project has moved to a new repository. If you're looking to contribute or access the latest version, please visit:

    https://github.com/paylinkmcp/paylink

    A Model Context Protocol (MCP) server designed to integrate AI applications with Safaricom's Daraja API, enabling seamless interaction with M-Pesa services.

    ⚠️ Warning: Not Production Ready

    This project is currently in development and is not recommended for production use. It's designed for:

    - Learning and experimentation

    - Development and testing environments

    - Proof of concept implementations

    For production use, please ensure:

    - Thorough security testing

    - Proper error handling

    - Complete implementation of all planned features

    - Compliance with Safaricom's production requirements

    What is an MCP Server?

    MCP (Model Context Protocol) servers provide capabilities for LLMs to interact with external systems. MCP servers can provide three main types of capabilities:

    • Resources: File-like data that can be read by clients (like API responses)
    • Tools: Functions that can be called by the LLM (with user approval)
    • Prompts: Pre-written templates that help users accomplish specific tasks

    Daraja MCP specifically leverages this architecture to connect AI systems with Safaricom's Daraja M-Pesa API.

    Overview

    Daraja MCP is a bridge between AI, fintech, and M-Pesa, making AI-driven financial automation accessible and efficient. By standardizing the connection between LLMs (Large Language Models) and financial transactions, Daraja MCP allows AI-driven applications to process payments, retrieve transaction data, and automate financial workflows effortlessly.

    Key Capabilities

    • ✅ AI-Powered M-Pesa Transactions – Enable LLMs to handle B2C, C2B, and B2B payments
    • ✅ Standardized Integration – MCP ensures compatibility with multiple AI tools
    • ✅ Secure & Scalable – Implements OAuth authentication and supports enterprise-grade transaction handling
    • ✅ Flexible Automation – AI agents can query account balances, generate invoices, and automate reconciliation

    Requirements

    • Python 3.12
    • Safaricom Daraja API Credentials (Consumer Key and Secret)

    Installation

    Step 1: Setting Up Your Environment

    1. Install uv Package Manager

    For Mac/Linux:

    bash
    curl -LsSf https://astral.sh/uv/install.sh | sh

    For Windows (PowerShell):

    powershell
    powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

    2. Clone the Repository

    bash
    git clone https://github.com/jameskanyiri/DarajaMCP.git
       cd DarajaMCP

    3. Create and Activate a Virtual Environment

    bash
    uv venv
       source .venv/bin/activate  # On Windows: .venv\Scripts\activate

    ✅ Expected Output: Your terminal prompt should change, indicating the virtual environment is activated.

    4. Install Dependencies

    bash
    uv sync

    Step 2: Setting up Environment Variables

    1. Copy the example environment file:

    bash
    cp .env.example .env

    2. Update the .env file with your actual credentials and configuration values.

    Note: For development, use the sandbox environment. Switch to the production URL when ready.

    Usage

    Testing with Claude Desktop

    1. Install Claude Desktop

    • Download and install the latest version from Claude Desktop
    • Make sure you're running the latest version

    2. Configure Claude Desktop

    • Open your Claude Desktop configuration file:
    bash
    # On MacOS/Linux
         code ~/Library/Application\ Support/Claude/claude_desktop_config.json
    
         # On Windows
         code %APPDATA%\Claude\claude_desktop_config.json
    • Create the file if it doesn't exist

    3. Add Server Configuration

    Choose one of the following configurations:

    #### Anthropic's Recommended Format

    json
    {
         "mcpServers": {
           "daraja": {
             "command": "uv",
             "args": [
               "--directory",
               "/ABSOLUTE/PATH/TO/PARENT/FOLDER/DarajaMCP",
               "run",
               "main.py"
             ]
           }
         }
       }

    #### Working Configuration (Tested)

    json
    {
         "mcpServers": {
           "DarajaMCP": {
             "command": "/ABSOLUTE/PATH/TO/PARENT/.local/bin/uv",
             "args": [
               "--directory",
               "/ABSOLUTE/PATH/TO/PARENT/FOLDER/DarajaMCP",
               "run",
               "main.py"
             ]
           }
         }
       }

    Note:

    - Replace /ABSOLUTE/PATH/TO/PARENT with your actual path

    - To find the full path to uv, run:

    bash
    # On MacOS/Linux
       which uv
    
       # On Windows
       where uv

    4. Verify Configuration

    • Save the configuration file
    • Restart Claude Desktop
    • Look for the hammer 🔨 icon in the interface
    • Click it to see the available tools:
    • generate_access_token
    • stk_push (Future Implementation)
    • query_transaction_status (Future Implementation)
    • b2c_payment (Future Implementation)
    • account_balance (Future Implementation)

    Tools and Prompts

    Payment Tools

    stk_push

    Initiate an M-Pesa STK push request to prompt the customer to authorize a payment on their mobile device.

    Inputs:

    • amount (int): The amount to be paid
    • phone_number (int): The phone number of the customer

    Returns: JSON formatted M-PESA API response

    generate_qr_code

    Generate a QR code for a payment request that customers can scan to make payments.

    Inputs:

    • merchant_name (str): Name of the company/M-Pesa Merchant Name
    • transaction_reference_no (str): Transaction reference number
    • amount (int): The total amount for the sale/transaction
    • transaction_type (Literal["BG", "WA", "PB", "SM", "SB"]): Transaction type
    • credit_party_identifier (str): Credit Party Identifier (Mobile Number, Business Number, Agent Till, Paybill, or Merchant Buy Goods)

    Returns: JSON formatted M-PESA API response containing the QR code data

    Payment Prompts

    stk_push_prompt

    Generate a prompt for initiating an M-Pesa STK push payment request.

    Inputs:

    • phone_number (str): The phone number of the customer
    • amount (int): The amount to be paid
    • purpose (str): The purpose of the payment

    Returns: Formatted prompt string for STK push request

    generate_qr_code_prompt

    Generate a prompt for creating an M-Pesa QR code payment request.

    Inputs:

    • merchant_name (str): Name of the merchant/business
    • amount (int): Amount to be paid
    • transaction_type (str): Type of transaction (BG for Buy Goods, WA for Wallet, PB for Paybill, SM for Send Money, SB for Send to Business)
    • identifier (str): The recipient identifier (till number, paybill, phone number)
    • reference (str, optional): Transaction reference number. If not provided, a default will be used.

    Returns: Formatted prompt string for QR code generation

    Document Processing Tools

    create_source

    Create a connector from data source to unstructured server for processing.

    Inputs:

    • connector_name (str): The name of the source connector to create

    Returns: Source connector details including name and ID

    create_destination

    Create a connector from unstructured server to destination for data storage.

    Inputs:

    • connector_name (str): The name of the destination connector to create

    Returns: Destination connector details including name and ID

    create_workflow

    Create a workflow to process data from source connector to destination connector.

    Inputs:

    • workflow_name (str): The name of the workflow to create
    • source_id (str): The ID of the source connector
    • destination_id (str): The ID of the destination connector

    Returns: Workflow details including name, ID, status, type, sources, destinations, and schedule

    run_workflow

    Execute a workflow.

    Inputs:

    • workflow_id (str): The ID of the workflow to run

    Returns: Workflow execution status

    get_workflow_details

    Get detailed information about a workflow.

    Inputs:

    • workflow_id (str): The ID of the workflow to get details

    Returns: Workflow details including name, ID, and status

    fetch_documents

    Fetch documents analyzed during workflow execution.

    Inputs: None

    Returns: List of analyzed documents

    Prompts

    create_and_run_workflow_prompt

    Generate a prompt to create and run a workflow for document processing.

    Inputs:

    • user_input (str): The user's processing requirements

    Returns: Formatted prompt for workflow creation and execution

    Example:

    python
    # Example usage
    prompt = await create_and_run_workflow_prompt(
        user_input="Process all PDF invoices from the invoices folder and store them in the processed folder"
    )
    # Returns: "The user wants to achieve Process all PDF invoices from the invoices folder and store them in the processed folder. Assist them by creating a source connector and a destination connector, then setting up the workflow and executing it."

    Resources

    Currently, no resources are available.

    License

    MIT License

    Acknowledgments

    • Safaricom for providing the Daraja API
    • Anthropic for the MCP framework
    • Contributors to the project

    Contact

    For any inquiries, please open an issue on the GitHub repository.

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