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Company financials from SEC XBRL

company_financials

Retrieve a US company's quarterly and annual revenue, net income, EPS, cash flow, capex, buybacks, dividends, and balance sheet data from SEC XBRL filings by ticker symbol.

Instructions

Quarterly revenue, net income and diluted EPS (calendar quarters, fourth quarter derived from the annual report), annual revenue, net income, operating cash flow, capex, buybacks and dividends, and the latest balance sheet instants, from the company's own XBRL filings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesUS ticker symbol, e.g. GME. Class shares as on the SEC list, e.g. BRK-B.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full behavioral burden. It does disclose a notable derivation: fourth quarter is derived from the annual report. However, it does not state whether the data is read-only (though it appears to be a retrieval tool), whether it requires authentication, whether it is rate-limited, or how fresh the data is. For a data-retrieval tool with no annotations, this leaves significant gaps.

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 a single, dense sentence that front-loads the key metrics. It is efficient and avoids fluff, though the enumeration is lengthy and could be slightly more scannable with formatting. Every clause adds value, so it earns a 4.

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?

There is no output schema, so the description must convey return values, which it does by listing the financial metrics. However, it omits important contextual details for an agent, such as whether the data is point-in-time, how to handle missing quarters, or if there are prerequisites like a valid SEC filing. For a tool with no annotations and no output schema, it is adequate but incomplete.

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% and the single parameter (symbol) is well documented in the schema itself, including format examples and class share handling. The description adds no parameter-level information, which is acceptable given the complete schema. Baseline of 3 is appropriate.

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 names a specific resource (company XBRL financials) and enumerates exactly which metrics it returns: quarterly revenue, net income, diluted EPS, annual figures, operating cash flow, capex, buybacks, dividends, and balance sheet instants. It also states the source (the company's own XBRL filings), which distinguishes it from siblings like fund_portfolio or short_interest.

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 explicit guidance on when to use this tool versus alternatives. The description does not mention any sibling tool, nor does it state conditions or exclusions. It implies usage by describing the data returned, but an agent must infer the appropriate context.

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