About Up Bank MCP Server
Up Bank MCP Server connects AI agents to the Up Bank API for account balance and transaction information. It is an early-stage Python server that handles API tokens through environment configuration.
Key features
- 01Retrieve account balances
- 02View transaction history
- 03Handle API tokens through environment variables
- 04Implement an MCP-compliant server
Use cases
- 01Check an Up Bank account balance
- 02Review Up Bank transactions
MCP support
Exposes Up Bank account and transaction access to MCP clients through an MCP-compliant server.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEUp Bank MCP Server
An MCP (Model Context Protocol) server implementation for Up Bank's API, allowing AI agents to interact with Up Bank accounts.
Features
- Account balance checking
- Transaction history viewing
- Secure API token handling
- MCP-compliant server implementation
Requirements
- Python 3.10+
- Up Bank API token
- MCP SDK
Setup
- Clone the repository
- Create a virtual environment:
python -m venv venv source venv/bin/activate # On Unix/macOS - Install dependencies:
pip install -r requirements.txt - Create a
.envfile with your Up Bank API token:UP_TOKEN=your_token_here
Development
Currently in initial development phase. Features being implemented:
- Basic account information retrieval
- Transaction history viewing
- MCP server implementation
Security
- API tokens are stored in
.envfile (not committed to repository) - Using environment variables for sensitive data
- Following Up Bank API security best practices
License
MIT License
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.