I've been building AI infrastructure for East Africa for the past 18 months. Seven MCP servers. Seven coordination failures. One underlying theory.
Markets fail when participants lack the information to coordinate. In East Africa, the failures are concrete:
Each of these is an information asymmetry problem. The technology to solve them exists. What's missing is the query layer.
mpesa-mcp → payment execution + transaction intelligence
bima-mcp → insurance products + parametric risk scoring
mkopo-mcp → alternative credit scoring from M-PESA patterns
soko-mcp → commodity price intelligence across 8 markets
sifa-mcp → portable reputation + skills passports
kazi-mcp → labor market matching + wage benchmarking
wapimaji-mcp → drought phase data across 47 counties
The power isn't any single tool — it's the combinations. An AI agent helping a farmer can simultaneously:
That's five coordination problems solved in a single agent session. Previously, solving any one of them required navigating multiple institutions, phone calls, and days of waiting.
The second layer is execution: tools that don't just provide information but actually trigger actions — STK Push payments, insurance policy enrollment, credit applications. Some of that already exists in mpesa-mcp. The rest is coming.
The goal isn't an app. It's infrastructure. Apps are built on top; they can be Swahili-native, SMS-delivered, USSD-based, or voice-first. The coordination layer stays constant.
All seven servers are live on PyPI. All are indexed on the Glama MCP directory. All are MIT-licensed.
pip install mpesa-mcp bima-mcp mkopo-mcp soko-mcp sifa-mcp kazi-mcp wapimaji-mcp