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Dividends & buybacks

get_capital_returns
Read-onlyIdempotent

How a company returns capital: dividend-rate history with each raise or cut, buyback authorizations beside the cash actually spent, splits and name changes. Forward ex-dates are not served. Use when the user asks: did X raise its dividend; is X buying back stock; has X cut it. Args: entity; kind = dividends | buybacks | actions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo"dividends" (default): the declared per-share dividend over time, each raise or cut named; "buybacks": repurchase authorisations beside the cash actually spent; "actions": stock splits and name changes; "calendar": the declared dividends (forward ex-dates are not served).dividends
limitNoMost records to return.
entityYesThe company: a ticker (AAPL, BRK.B), a company name (Apple) or an SEC CIK.
periodsNoHow many of the latest filed periods to read (usually quarters).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds a scope boundary (forward ex-dates are not served) but does not elaborate on return format, data freshness, or sources beyond what the schema and output schema imply; with an output schema present, a 3 is appropriate.

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 resource definition, followed by scope boundaries and usage examples in compact sentences; every sentence adds information and there is no waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only data-retrieval tool with full schema coverage and an output schema, the description covers what is returned, what is excluded (forward ex-dates), and when to use it. It leaves minor gaps around data recency and output shape, but those are not required given the output schema.

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 schema already describes entity, kind with its enum meanings, limit, and periods in detail. The description only restates 'Args: entity; kind = dividends | buybacks | actions' without adding new semantics, so it meets the baseline 3 for a fully documented schema.

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 (capital returns: dividend history, buybacks, splits, name changes) with a clear framing verb phrase, and lists exactly what records are returned. It is distinguishable from siblings like get_calendar or get_insider_trades.

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?

It provides explicit example questions that map directly to the tool (did X raise its dividend; is X buying back stock; has X cut it), which is strong when-to-use guidance. It does not name alternatives or when-not-to-use cases, keeping it below 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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