FinBridge

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.

Not this tool for: Korean statements (get_dart_financials), a KR-vs-US pair on one screen (compare_financials_kr_us), or ranking many companies at once (query_db, which reads the stored table and covers KR/US/TW/JP/EU). This tool requests one US company's SEC XBRL company facts through the source adapter; results may be reused from a process-local cache for up to 24 hours. data_as_of.generated_at is response creation time, not source retrieval time. Peer comparisons use separately dated database snapshots.

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

Name	Required	Description	Default
`freq`	No	'annual' = fiscal years from 10-K filings; 'quarterly' = discrete Q1-Q3 from 10-Q filings	annual
`company`	Yes	US company: ticker (e.g. 'AAPL', 'BRK-B' or 'BRK.B'), company name, or CIK number
`metrics`	No	Optional metric subset. Available: revenue, gross_profit, operating_income, net_income, eps_diluted, assets, liabilities, equity, cash_and_equivalents, operating_cash_flow. Default: all
`periods`	No	Number of most-recent periods (default 3)
`response_format`	No	'markdown' for a table, 'json' for compact machine-readable output	markdown

Output Schema

TableJSON Schema

Name	Required	Description	Default
`basis`	No
`notes`	No
`company`	Yes
`periods`	Yes
`data_as_of`	No
`plan_limit`	No
`peer_comparison`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 7, 2026

addedOutput schema / properties / notes

Added value: +{
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}

Changed3 schema fields changedSep 7, 2026

addedOutput schema / properties / data_as_of

Added value: +{
+  "anyOf": [
+    {
+      "additionalProperties": true,
+      "properties": {},
+      "type": "object"
+    },
+    {
+      "type": "null"
+    }
+  ]
+}

addedOutput schema / properties / peer_comparison

Added value: +{
+  "anyOf": [
+    {
+      "additionalProperties": true,
+      "properties": {},
+      "type": "object"
+    },
+    {
+      "type": "null"
+    }
+  ]
+}

addedOutput schema / properties / plan_limit

Added value: +{
+  "additionalProperties": true,
+  "properties": {},
+  "type": "object"
+}

First observedSep 6, 2026

TDQS

A5/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds substantial behavioral context: results may be cached up to 24 hours, data_as_of.generated_at is response creation time not retrieval time, peer comparisons use separately dated snapshots, Q4 is not reported separately for quarterly, and unsupported companies (funds, 20-F filers) return explanatory errors. These details go well beyond the annotations and significantly aid correct invocation.

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?

The description is long but well-structured and front-loaded: it opens with the core purpose, then exclusions, then detailed parameter explanations, return format, examples, and usage guidance. Every section adds value; there is no filler. The structure mirrors the tool's complexity and makes the information easy to scan.

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?

Given the tool's complexity (5 parameters, output schema, error cases, sibling tools), the description is fully complete. It covers all parameters with nuanced explanations, describes the return structure (NormalizedFinancials with periods), provides usage examples, names sibling alternatives, and explains error behavior for unknown or unsupported companies. Nothing an agent needs to call it correctly is missing.

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

Parameters5/5

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

Although schema coverage is 100%, the description adds meaning beyond the schema: it explains the meaning of 'annual' vs 'quarterly' (Q4 not separately reported), that periods are most-recent first, that values are raw USD, and that eps_diluted is per share. It also provides concrete examples mapping natural-language queries to parameter values ('Apple's revenue...' -> {company:'AAPL', metrics:['revenue','net_income']}). This fully compensates any gaps and enhances 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 precise verb ('get') and resource ('US company financials from SEC EDGAR XBRL company facts (US-GAAP)'), and specifies scope (annual 10-K, quarterly 10-Q, raw USD). It explicitly differentiates from siblings like get_dart_financials and compare_financials_kr_us, so an agent can instantly tell what it does and what it is not.

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 gives explicit 'Not this tool for' guidance naming specific alternatives (get_dart_financials, compare_financials_kr_us, query_db) and conditions ('Korean statements', 'KR-vs-US pair', 'ranking many companies'). It also provides 'Use when' and 'Don't use for' statements, leaving no ambiguity about selection.

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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