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chrischall

Credit Karma MCP

by chrischall

ck_get_spending_by_merchant

Read-only

Return top merchants by total debit spend.

Instructions

Return top merchants by total debit spend.

Input Schema

NameRequiredDescriptionDefault
limitNoDefault 25; a whole number from 1 to 500
categoryNoPartial category name filter
end_dateNoYYYY-MM-DD
start_dateNoYYYY-MM-DD

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv3.3.3
    • changedInput schema / properties / limit / description
      Previous value: -"Default 25"New value: +"Default 25; a whole number from 1 to 500"
    • addedInput schema / properties / limit / maximum
      Added value: +500
    • addedInput schema / properties / limit / minimum
      Added value: +1
    • changedInput schema / properties / limit / type
      Previous value: -"number"New value: +"integer"
  2. Changed1 schema field changedv3.0.0
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  3. First observedv2.3.1

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, covering the safety profile, so the bar is lower. The description usefully clarifies the ranking semantics ('top' by total debit spend), but says nothing about what happens when date ranges are omitted, tie-breaking, or result shape.

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?

A single sentence with no filler and the key qualifier ('by total debit spend') front-loaded. It is efficient, though arguably terse for a four-parameter aggregation tool that needs date-scope clarification.

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

Completeness3/5

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

With no output schema and no annotations about returns, the description leaves the date-range default behavior unspecified — does omitting start_date/end_date mean all-time or a default period? That is a meaningful gap for a spending aggregation, though the core purpose is conveyed.

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 limit, category, start_date and end_date all documented in the schema, so the schema carries the parameter burden. The description adds no parameter detail (e.g., that start/end dates are optional or how 'top' ordering interacts with limit), so the baseline 3 applies.

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?

States a specific verb ('Return') and a well-defined resource ('top merchants by total debit spend'), including the aggregation dimension (debit spend). It implicitly distinguishes itself from ck_get_spending_by_category by grouping on merchants rather than categories, but it never names or explicitly contrasts that sibling.

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

Usage Guidelines2/5

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

There is no guidance on when to pick this over ck_get_spending_by_category or ck_list_transactions, and no prerequisites or exclusions are stated. The agent must infer the use case from the tool name alone.

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