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mkopo-mcp

mkopo-mcp

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github.com/gabrielmahiaFinance0Updated 6d ago
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About mkopo-mcp

mkopo-mcp is a demonstration MCP server that analyzes synthetic M-PESA behavioral signals for alternative credit scoring in Kenya. Its tools generate scores, repayment-capacity analysis, reports, lender-type matches, and improvement tips; results are not real credit decisions.

Key features

  1. 01Alternative scores from M-PESA signals
  2. 02Inflows and outflows analysis
  3. 03Structured credit report summaries
  4. 04Matching to Kenyan lender types
  5. 05Credit-tier improvement tips
  6. 06Automated-decision notices

Use cases

  1. 01Demonstrating mobile-money-based scoring
  2. 02Exploring loan capacity from transaction patterns
  3. 03Generating sample loan-review reports
  4. 04Showing credit-improvement suggestions

MCP support

Exposes five credit-analysis tools to MCP clients, covering scoring, cash-flow analysis, report summaries, lender-type matching, and improvement tips. Results use synthetic demo data and are not suitable for operational decisions.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "gabrielmahia-mkopo": {
      "command": "uvx",
      "args": [
        "mkopo-mcp"
      ]
    }
  }
}
Run commanduvx mkopo-mcp

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypimkopo-mcp0.1.9

Tools

5

Saved tool list · updated 6d ago

ParameterTypeRequiredDescription
has_fuliza_debtbooleanYesCurrently has unpaid Fuliza (Safaricom overdraft) balance
months_as_mpesa_userintegerYesHow many months the person has used M-PESA (0-240)
has_savings_behaviourbooleanYesMakes regular small savings (M-Shwari, chama, SACCO, KCB M-PESA)
pays_utilities_on_timebooleanYesHas paid KPLC or water bills via M-PESA in past 6 months
has_loan_default_historybooleanYesHas defaulted on any previous loan (M-Shwari, KCB, bank, chama)
has_multiple_income_streamsbooleanYesReceives income from more than one source (multiple employers, business + salary)
has_regular_income_depositsbooleanYesReceives regular deposits (salary, business income) at least twice monthly

Details

From the project README

💳 mkopo-mcp — Alternative Credit Scoring MCP Server

mkopo-mcp Glama score smithery badge

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.

  • Regular income deposits → income stability
  • Utility payment regularity → discipline signal
  • Savings behaviour (M-Shwari, chama) → future orientation
  • Fuliza usage ratio → short-term distress indicator
  • Business Paybill receipts → verifiable commercial activity

Tools

ToolWhat it does
alternative_credit_scoreScore from M-PESA behavioural signals (300–850 scale)
mpesa_creditworthinessAnalyse inflow/outflow patterns for loan capacity
credit_report_summaryGenerate structured report like a loan officer would review
loan_eligibilityMatch to appropriate Kenya lender types
credit_improvement_tipsPersonalised steps to improve credit tier

Quick Start

pip install mkopo-mcp       # coming soon to PyPI
mkopo-mcp                   # starts on stdio

Research Basis

  • World Bank "Expanding Access to Finance" (2022)
  • Breza & Kinnan "Measuring the Equilibrium Impacts of Credit" (2021)
  • CBK Prudential Guidelines (2022)
  • Kenya CRB Act (Cap. 44A)

⚠️ DEMO data — not a real CRB product. Consult Metropol or CRB Africa for official reports.

© 2026 Gabriel Mahia / AI Kung Fu LLC · MIT License

Part of the East Africa Coordination Stack

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.

pip install africa-coord-bus

All servers: pypi.org/user/gmahia Live demo: coord-cascade-demo

IP & Collaboration

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

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: a Senate committee recommended abandoning it in October 2026, so as of 2026-10-23 Kenya has no dedicated AI law and the Data Protection Act is what applies. The bill would have added a right to an explanation and to human review of significant automated decisions such as a loan rejection, and commentators expected credit scoring to be treated as high-risk under such a law. 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.

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