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mkopo-mcp

mkopo-mcp

by gabrielmahia
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

loan_eligibility

Read-only

Estimate loan eligibility across Kenya lender types based on monthly income, credit tier, loan purpose, and requested amount. Compare potential offers from multiple lenders.

Instructions

Estimate loan eligibility across Kenya lender types. Western parallel: LendingTree loan comparison, Bankrate. DEMO — not actual loan offers.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "monthly_income_kes": "<monthly_income_kes>",
      "credit_tier": "<credit_tier>",
      "loan_purpose": "<loan_purpose>",
      "requested_amount_kes": "<requested_amount_kes>"
    },
    "name": "loan_eligibility"
  }
}

Input Schema

NameRequiredDescriptionDefault
credit_tierYesCredit tier from alternative_credit_score: PRIME, NEAR_PRIME, SUBPRIME, THIN_FILE
loan_purposeYesLoan purpose: business_working_capital, home_improvement, education, emergency, agriculture, equipment
monthly_income_kesYesMonthly income in KES
requested_amount_kesYesRequested loan amount in KES

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=true, and the description adds critical behavioral context: 'DEMO — not actual loan offers.' This discloses the tool's non-production nature, which is valuable beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero waste. First sentence states core function, second provides context and demo caveat. Efficiently structured for quick comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and annotations covering safety, the description is adequate for a demo tool. It could elaborate on the format of the estimate, but completeness is sufficient given the demo nature.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all 4 parameters. The description does not add further detail beyond what the schema provides, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it estimates loan eligibility for Kenya lenders, with a Western parallel and a demo notice. It distinguishes from sibling tools focused on credit scores and tips, but could be more specific about the nature of eligibility estimation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for estimating loan eligibility, but does not explicitly state when to use versus alternatives like alternative_credit_score or mpesa_creditworthiness. The Western parallel provides context but no exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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