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FinBridge

by kr.gronox.mcp

Get US Company Financials (SEC XBRL)

get_edgar_financials
Read-only

Normalized annual (10-K) or quarterly (10-Q) financial statements for a US company, from SEC EDGAR XBRL company facts (US-GAAP). Values are raw USD (not scaled); eps_diluted is USD per share.

Args:

  • company (required): ticker / company name / CIK (e.g. 'AAPL', 'Microsoft', '789019')

  • freq: 'annual' (default, from 10-K) or 'quarterly' (discrete Q1-Q3 from 10-Qs; Q4 is not reported separately)

  • periods: how many most-recent periods, 1-12 (default 3)

  • metrics: optional subset of [revenue, gross_profit, operating_income, net_income, eps_diluted, assets, liabilities, equity, cash_and_equivalents, operating_cash_flow] (default all)

  • response_format: 'markdown' (default) or 'json'

Returns NormalizedFinancials: {company:{name, id(CIK), ticker}, basis:'US-GAAP (10-K)', periods:[{period:'FY2024', fiscal_year, end, currency:'USD', metrics:{revenue, net_income, ...}}], notes}. periods are most-recent first; fiscal_year = calendar year of the period end date.

Examples:

  • "Apple's revenue and net income for the last 3 years" -> {company:'AAPL', metrics:['revenue','net_income']}

  • "MSFT last 4 quarters" -> {company:'MSFT', freq:'quarterly', periods:4}

Use when: you need US-GAAP fundamentals for a US-listed company. Don't use for: Korean companies (get_dart_financials), stock prices, or IFRS 20-F foreign private issuers (not supported).

Errors: unknown company -> use search_edgar_company first; companies without us-gaap XBRL facts (funds, 20-F filers) return an error explaining why.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
freqNo'annual' = fiscal years from 10-K filings; 'quarterly' = discrete Q1-Q3 from 10-Q filingsannual
companyYesUS company: ticker (e.g. 'AAPL', 'BRK-B' or 'BRK.B'), company name, or CIK number
metricsNoOptional metric subset. Available: revenue, gross_profit, operating_income, net_income, eps_diluted, assets, liabilities, equity, cash_and_equivalents, operating_cash_flow. Default: all
periodsNoNumber of most-recent periods (default 3)
response_formatNo'markdown' for a table, 'json' for compact machine-readable outputmarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
basisNo
companyYes
periodsYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

The description goes well beyond the readOnlyHint annotation by disclosing that values are raw USD, eps_diluted is per share, Q4 is not reported separately, periods are most-recent first, and fiscal_year equals the calendar year of the period end date. It also documents error behavior for unknown companies and unsupported filers, adding substantial behavioral context beyond what annotations provide.

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?

The description is well-structured with clear sections: summary, Args, Returns, Examples, Use when, and Errors. It is longer than necessary because the Args section repeats schema information, but every section serves a distinct purpose and the critical usage guidance is front-loaded.

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?

The description is complete for a read-only financial data tool: it covers input semantics, output shape, edge cases (Q4 missing, foreign issuers, unknown companies), alternative tools, and usage contexts. The output schema is also summarized in the Returns section, so an agent has enough information to invoke and interpret the tool correctly.

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?

Schema coverage is 100%, so the baseline is 3. The description adds value through concrete examples (e.g., 'AAPL', 'Microsoft', '789019'), clarifications like discrete Q1-Q3 from 10-Qs, and a natural-language mapping example. There is some duplication of schema descriptions in the Args block, but the additions improve parameter understanding.

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?

The description states a specific verb and resource: it 'Normalized annual (10-K) or quarterly (10-Q) financial statements for a US company, from SEC EDGAR XBRL company facts (US-GAAP).' It clearly distinguishes itself from siblings like get_dart_financials and get_stock_prices by specifying US-GAAP fundamentals from SEC filings.

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?

The description explicitly says 'Use when: you need US-GAAP fundamentals for a US-listed company' and 'Don't use for: Korean companies (get_dart_financials), stock prices, or IFRS 20-F foreign private issuers (not supported).' This names alternatives and exclusion conditions, giving an agent clear routing 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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TDQS

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct resource+action targets, and the overlapping screen_* tools are thoroughly cross-referenced with 'use screen_X instead' guidance. Minor ambiguity exists between get_disclosure_feed, get_dart_filings, and get_dart_major_events, which all surface KR filings from different angles but remain distinguishable.

Naming Consistency5/5

Every tool follows a consistent verb_noun snake_case pattern: get_* for retrievers, screen_* for screeners, search_* for lookups, plus action verbs like analyze_, backtest_, compare_, import_, and query_. Subfamilies (dart_*, edgar_*, fred_*, crypto_*) are consistently prefixed, making tool selection predictable.

Tool Count3/5

37 tools is heavy, and the four momentum screeners (canslim/kell/minervini/schwartz) plus three KR disclosure tools could arguably be collapsed into parameterized variants. However, the server's unusually broad scope—KR/US/TW/JP/EU equities, crypto, macro, portfolio, backtesting—means most tools earn their place, so the count is high but not chaotic.

Completeness4/5

The surface covers the core workflow well: search, prices, fundamentals, filings, insider trades, valuation, screeners, backtesting, and portfolio tracking for KR/US, plus crypto and macro. Notable gaps are the lack of single-company financial-statement tools for TW/JP/EU (only available through screen_companies) and no real-time stock quotes, but these are workable for the stated local-database research purpose.

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