Demo alternative credit scoring from M-PESA signals. Not a CRB product; synthetic, nothing stored.
💳 mkopo-mcp — Alternative Credit Scoring MCP Server
mkopo-mcp is an MCP (Model Context Protocol) server for credit scoring, described as an “Alternative Credit Scoring” MCP server. It is positioned for use with AI agents and is compatible with claude-sonnet-5. The package can be installed via Python and used with any MCP client.
🛠️ Key Features
Alternative credit scoring functionality via an MCP server
Open-source server package distributed as a Python install (pip install mkopo-mcp)
Compatibility noted with claude-sonnet-5
🚀 Use Cases
Credit scoring workflows executed through MCP tools
Developer integration of an MCP server into agent-based systems
Use in East Africa / Kenya-focused fintech contexts (per listed topics)
⚡ Developer Benefits
MCP compatibility for tool-based agent interactions
Python installation path
Topics include mcp, python, and ai-agents
⚠️ Limitations
Source material does not describe specific tools, inputs, or outputs beyond compatibility and installation details
Compatible with claude-sonnet-5 (released 2026-06-30) — Anthropic's most agentic
Sonnet yet. Runs multi-step tool chains end-to-end without stopping short.
Install: pip install mkopo-mcp · Use with any MCP client.
Most East Africans transacting heavily on mobile money have no formal credit file — the data exists in M-PESA history, but no scoring model reads it as an agent-callable tool.
70%+ of Kenyan adults are "credit invisible" — no formal credit history means no access to capital. M-PESA is a de facto financial history record that predicts repayment behaviour. mkopo-mcp makes that intelligence accessible to AI agents.
The Structural Problem
In mature economies, credit bureaus have 30+ years of credit card, mortgage, and auto loan data per person. That data machine took 50 years to build. In Kenya, most people's financial lives happen in cash and mobile money — invisible to formal credit systems.
M-PESA is the credit bureau that doesn't know it is one.
This MCP server is part of the Kenya coordination infrastructure.
Connect it to africa-coord-bus —
the coordination event bus that routes signals between domains automatically.
MIT licensed. Feedback via GitHub Issues only — pull requests are not accepted. Demo data is labeled DEMO and is not suitable for operational decisions. Full policy: docs/architecture/IP_POLICY.md. Security reports: see SECURITY.md.
Part of the East Africa coordination stack
Install & run:pip install reli-cli && reli list — the MCP servers on the official MCP Registry under io.github.gabrielmahia
Model-agnostic by design: closed APIs, open-weight models, and small distilled models are all first-class citizens.
Demonstration only, and Kenya's rules on automated decisions
These tools produce synthetic demonstration results, not credit decisions, and they are not a Credit Reference Bureau product. Do not pass a real person's name or ID number to them.
If you build something real on a score like this, note the law: in Kenya the Data Protection Act 2019 is in force, including its provisions on decisions made solely by automated processing (section 35). The Artificial Intelligence Bill 2026 is a Senate bill and is not law (as of 2026-10-10); it would add a right to an explanation and to human review of significant automated decisions such as a loan rejection, and commentators expect credit scoring to be treated as high-risk. Every scoring answer from this server carries an automated_decision_notice saying so. The lender examples are generic lender types, not named companies: an earlier version listed real lenders by tier, which this synthetic score cannot support.
