TipRanks

get_company_kpis

Read-onlyIdempotent

Returns company-specific operational KPIs: segment revenue, geographic breakdown, active users/devices, gross margins by type, opex breakdown.

Full history runs back to 2014 and is large, so this returns the most
recent `count` periods per metric by default — pass count=0 for the full
history, or `group` to fetch a single group.

Args:
    ticker: Stock ticker (e.g. 'AAPL', 'MSFT')
    count: Most-recent periods per metric (default 8, max 40; 0 = full history)
    group: Optional single group by slug or title

Returns JSON: {"ticker", "lastUpdated", "groups": [...]}. Each group has
slug, title, and metrics: [{title, slug, unit, series: [{periodEnd, value}]}].

Input Schema

TableJSON Schema

Name	Required	Description
`count`	No
`group`	No	Optional single KPI group by slug or title (e.g. 'revenue_geography', 'Revenue by Segment'). Omit for all groups.
`ticker`	Yes

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. The description adds valuable behavioral context: the data is large (history back to 2014), the default limited to recent periods, and the exact return structure. This goes beyond what annotations provide and outlines expected behavior.

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 concise and well-structured: a clear purpose sentence, a brief note on data size and defaults, the Args list, and the return format. Every sentence adds meaningful information without redundancy.

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 lack of an output schema, the description provides a complete return format with nested structure. It covers all three parameters, default behavior, and return fields, making it self-sufficient for an AI agent to understand what the tool does and what it will return.

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?

Schema description coverage is only 33% (only 'group' has a description), but the description's Args section explains all three parameters including default count (8), max count (40), the special value 0 for full history, and that group accepts slug or title. This fully compensates for the schema gaps and adds crucial semantics.

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 clearly states the tool 'Returns company-specific operational KPIs' and lists concrete examples (segment revenue, geographic breakdown, active users/devices, gross margins by type, opex breakdown). This specific verb+resource combination distinguishes it from sibling tools like get_financials or get_earnings_*.

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 provides clear context on how to use the tool: it explains the default behavior (most recent count periods), when to use count=0 for full history, and how to fetch a single group with the group parameter. However, it does not explicitly mention alternatives or when-not-to-use this tool versus siblings, hence not a 5.

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

A3.8/5.0

Disambiguation3/5

Many tools have overlapping purposes, e.g. get_etf_analysis vs get_etf_forecast both provide ETF analyst consensus, get_etf_holdings vs get_etf_top_stocks both list constituents, and get_portfolio_overview vs get_portfolio_performance both return returns/performance. The detailed descriptions help, but the sheer number of similar tools creates ambiguity in selection.

Naming Consistency4/5

The set is largely consistent with a 'get_' prefix and descriptive nouns (get_stock_quotes, get_crypto_quote, get_dividend_history). Minor deviations include 'list_my_portfolios' instead of 'get_my_portfolios' and singular/plural variants like get_all_commodities_quotes vs get_commodity_quote, but the pattern remains predictable.

Tool Count1/5

With 71 tools, the count far exceeds the 50+ threshold described as an extreme mismatch. Even though the server covers a broad financial domain, such a large surface is overwhelming for an agent and includes many redundant or highly specific tools that could be consolidated.

Completeness5/5

The tool set provides comprehensive coverage of TipRanks data: quotes and historical data for all major asset classes, news, earnings and economic calendars, analyst and sentiment data, financial statements, technical analysis, options, portfolios, and screeners. There are no obvious dead ends for typical financial research tasks.

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