FinBridge

Compare KR vs US Company Financials

compare_financials_kr_us

Read-only

Compare annual financial statements of a Korean listed company (source: OpenDART, K-IFRS) and a US listed company (source: SEC EDGAR, US-GAAP) side by side, with KRW values converted to USD using Federal Reserve H.10 annual-average exchange rates.

Args:

kr_company: Korean company name / 6-digit stock code / DART corp_code (e.g. '삼성전자', '005930')
us_company: US ticker / name / CIK (e.g. 'AAPL', 'Apple')
years: number of recent fiscal years, 1-5 (default 3; free plan up to 4)
metrics: subset of [revenue, gross_profit, operating_income, net_income, eps_diluted, assets, liabilities, equity, cash_and_equivalents, operating_cash_flow]
response_format: 'markdown' (default) or 'json'

Returns per-metric, per-year rows: {fiscal_year, kr_krw, kr_usd, us_usd, ratio_kr_over_us} plus the FX rates used and accounting-basis caveats.

Examples:

"삼성전자 vs Apple 최근 3년 매출·영업이익 비교" -> {kr_company:'삼성전자', us_company:'AAPL', metrics:['revenue','operating_income']}
Don't use for quarterly data (annual only) or non-KR/US companies.

Errors: unknown company names suggest using search_dart_company / search_edgar_company first.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`years`	No	How many recent fiscal years to compare (default 3; the free plan covers up to 4)
`metrics`	No	Metrics to compare (default: revenue, operating_income, net_income, assets, equity). Available: revenue, gross_profit, operating_income, net_income, eps_diluted, assets, liabilities, equity, cash_and_equivalents, operating_cash_flow
`kr_company`	Yes	Korean company: name (e.g. '삼성전자'), 6-digit stock code (e.g. '005930'), or 8-digit DART corp_code
`us_company`	Yes	US company: ticker (e.g. 'AAPL'), company name, or CIK
`response_format`	No	'markdown' for tables, 'json' for compact machine-readable output	markdown

Output Schema

TableJSON Schema

Name	Required	Description	Default
`fx`	No
`kr`	Yes
`us`	Yes
`notes`	No
`metrics`	No
`comparison`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 30, 2026

changedInput schema / properties / years / description

Previous value: -"How many recent fiscal years to compare (default 3)"New value: +"How many recent fiscal years to compare (default 3; the free plan covers up to 4)"

First observedSep 6, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, so the safety profile is carried by structured data. The description nonetheless adds real context beyond it: upstream sources (OpenDART K-IFRS, SEC EDGAR US-GAAP), the Fed H.10 annual-average FX basis, accounting-basis caveats, and a plan quota ('free plan up to 4' years). Only minor operational details (rate limits, latency) are absent.

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-loaded one-sentence summary followed by clearly delimited Args/Returns/Examples/Errors sections, each earning its place. Minor redundancy: the Args and metrics enumeration duplicate the schema's own descriptions, so a small amount of text could be trimmed without losing information.

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

Completeness5/5

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

Covers everything an agent needs: valid inputs, FX conversion basis, accounting-standard mismatch caveats, output shape, hard exclusions, and error recovery paths. An output schema exists so the return-value explanation is optional, yet the description still orients the agent correctly.

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 description coverage is 100%, so the baseline is 3 — every parameter (kr_company, us_company, years, metrics, response_format) is already documented in the schema, and the description's Args block largely restates it. The worked natural-language example does add value by showing how kr_company's three accepted identifier forms map to a real query.

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 (compare) and resource (annual financial statements of a Korean vs US listed company), plus the exact data sources and FX methodology. An agent can distinguish this immediately from get_dart_financials and get_edgar_financials, which cover only one market each.

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

Usage Guidelines5/5

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

Explicitly states when not to use it ('annual only', 'non-KR/US companies') and routes the agent to search_dart_company / search_edgar_company on unknown-name errors. This is a full when/when-not/alternative specification, not an implied one.

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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