Daraja MCP

Daraja MCP

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 ManagerFor Mac/Linux:
   curl -LsSf https://astral.sh/uv/install.sh | sh
   For Windows (PowerShell):
   powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
2. Clone the Repository
   git clone https://github.com/jameskanyiri/DarajaMCP.git cd DarajaMCP
3. Create and Activate a Virtual Environment
   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
   uv sync

Step 2: Setting up Environment Variables

1. Copy the example environment file:
   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:
     # 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

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

   Working Configuration (Tested)

   { "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:

   # 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:

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.