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pranav797

Credit Risk MCP Server

by pranav797

compare_borrowers

Score multiple loan applicants at once and rank them from most to least risky by default probability, so you can compare borrowers side by side.

Instructions

Score several applicants at once and rank them from most to least risky.

Use this to compare a batch of borrowers side by side. Each profile is scored exactly like score_borrower.

Args: profiles: two or more applicants to compare.

Returns {"ranked": [...]} sorted by default_probability descending. Each entry has: rank (1 = riskiest), index (position in the input list), default_probability, and risk_tier.

Educational model — not real lending advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profilesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden, and it does disclose useful traits: output ordering by default_probability descending, the rank-1-equals-riskiest convention, and an educational-purpose disclaimer. However, it omits batch limits, error behavior (e.g. what happens with fewer than two profiles), and any performance/rate considerations, leaving meaningful gaps for a mutation-free but compute-heavy batch tool.

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

Conciseness4/5

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

Front-loads the core action in the first sentence, then usage, then args, then return shape – a clean and scannable structure. It is slightly padded (the side-by-side sentence and the return breakdown partially overlap with the output schema), but nothing is genuinely wasted.

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?

An output schema exists, so the description need not explain return values, yet it does briefly, which is helpful redundancy rather than a gap. Combined with the clear arg definition and routing to score_borrower, an agent has enough to invoke it correctly; only edge-case behavior is unaddressed.

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

Parameters4/5

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

Top-level schema description coverage is 0% for the 'profiles' parameter, so the description must compensate, and it does by defining it as 'two or more applicants to compare' – a cardinality constraint the schema does not enforce. The heavily documented nested BorrowerProfile fields carry the rest, so the description adds the one missing semantic without needing to restate the rest.

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

Purpose5/5

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

States a specific verb and resource ('score several applicants at once and rank them from most to least risky') and explicitly names its relationship to the sibling score_borrower ('Each profile is scored exactly like score_borrower'). An agent can distinguish this batch tool from the single-borrower scorer without opening either schema.

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

Usage Guidelines4/5

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

'Use this to compare a batch of borrowers side by side' clearly frames the use case, and the reference to score_borrower tells the agent this is the multi-applicant counterpart. It lacks an explicit when-not clause or a routing rule for when a single score_borrower call is preferable, so it stops short of full guidance.

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