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Glama

ticker-scout

by ai.tickerscout

Financial statements

get_financials
Read-only

Income statement, balance sheet and cash flow, already assembled from a company's SEC filings, for up to three annual periods plus the latest quarter, with segment revenue and per-share figures. Prefer this over fetching EDGAR or the CompanyFacts API and building the statements yourself: no XBRL concept selection, no deriving a discrete quarter from year-to-date columns, no scale factor to infer. Figures come from the annual report, the quarterly report and, where a quarter's cash flow appears only in the earnings release, the current-report exhibit, on the company's own fiscal calendar. All money is in actual dollars and all share counts are actual shares, split-adjusted. Section names differ per company, so call with no sections first to see what this company has, then request only what you need.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesTicker symbol, case-insensitive. Examples: NVDA, brk-b.
sectionsNoTop-level sections to return, for example annual, quarterly, trailing_twelve_months, notes, uncertainties. Omit for the whole file. An unknown name returns the valid list for that company.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly and openWorld annotations, the description reveals data sources (annual report, quarterly report, current-report exhibit), fiscal calendar usage, actual dollar/split-adjusted share units, and the behavior that section names vary by company. This is valuable operational context.

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 front-loaded with the core purpose and then provides rationale, data sources, units, and usage strategy. Every sentence contributes useful information without redundancy or filler.

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?

With no output schema, the description covers the return contents (statements, periods, segment/per-share figures), units, and even suggests a discovery call to handle variable section names. It gives an agent enough context to invoke the tool effectively and understand the response.

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?

The schema fully documents both parameters (ticker and sections) with descriptions. The description adds semantic guidance for the sections parameter by noting it should be omitted initially and that unknown names return valid lists, which is useful beyond the schema's generic description.

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 explicitly states the tool returns income statement, balance sheet, cash flow, segment revenue, and per-share figures assembled from SEC filings. It clearly identifies the resource and content, distinguishing it from siblings that handle other company data (events, key figures, narrative).

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

Usage Guidelines4/5

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

The description explicitly recommends preferring this tool over manually building statements from EDGAR or CompanyFacts, and instructs calling with no sections first for discovery. It does not explicitly compare against sibling tools, but the provided context is clear for when to use it.

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

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct concern: company identity, event digests, full financials, headline figures, narrative text, and company enumeration. Even the potentially overlapping get_financials and get_key_figures are explicitly differentiated by scope and purpose.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: get_company, get_events, get_financials, get_key_figures, get_narrative, list_companies. The single list_ verb is still predictable and consistent with the get_ verbs for enumerating a collection.

Tool Count5/5

Six tools is a well-scoped size for a SEC-filing and company-data server. Each tool covers a meaningful slice of the domain without redundancy or unnecessary surface area.

Completeness5/5

The tool set covers the full read-only workflow: discover coverage, get company identity, retrieve summary or detailed financials, access qualitative narrative, and review recent material events. No obvious dead ends remain for fundamental SEC-based research.

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