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alpacahq

alpaca-mcp-server

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by alpacahq

get_corporate_action_announcements

Retrieve corporate action announcements including dividends, mergers, splits, and spinoffs from Alpaca's financial data, with filtering by date range, symbol, and action type.

Instructions

Retrieves corporate action announcements (dividends, mergers, splits, spinoffs). Use a narrow date range and filter by symbol when possible — broad queries can return very large responses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ca_typesYesA comma-delimited list of Dividend, Merger, Spinoff, or Split.
sinceYesThe start (inclusive) of the date range when searching corporate action announcements. This should follow the YYYY-MM-DD format. The date range is limited to 90 days.
untilYesThe end (inclusive) of the date range when searching corporate action announcements. This should follow the YYYY-MM-DD format. The date range is limited to 90 days.
symbolNoThe symbol of the company initiating the announcement.
cusipNoThe CUSIP of the company initiating the announcement.
date_typeNodeclaration_date, ex_date, record_date, or payable_date

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries full burden. It discloses a key behavioral trait: 'broad queries can return very large responses,' warning about potential performance/volume issues. However, it doesn't mention rate limits, authentication needs, or pagination behavior, leaving some gaps for a retrieval tool.

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?

Two sentences with zero waste: first states purpose with examples, second provides critical usage guidance. It's front-loaded with the core function and efficiently addresses the most important operational constraint.

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?

Given 6 parameters, 100% schema coverage, and an output schema (implied by context signals), the description is reasonably complete. It covers purpose, usage guidance, and a key behavioral warning. However, for a tool with potential large responses, mentioning pagination or result limits would enhance completeness.

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 description coverage is 100%, so the schema fully documents all 6 parameters. The description adds no parameter-specific information beyond what's in the schema, but it implies the importance of 'since' and 'until' (date range) and 'symbol' for filtering. Baseline 3 is appropriate when schema does the heavy lifting.

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 verb ('Retrieves') and resource ('corporate action announcements'), with specific examples (dividends, mergers, splits, spinoffs). It distinguishes from sibling tools like 'get_corporate_action_announcement' (singular) by implying this retrieves multiple announcements, and from 'get_corporate_actions' by specifying announcements rather than actions.

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?

Explicit guidance is provided: 'Use a narrow date range and filter by symbol when possible — broad queries can return very large responses.' This tells the agent when to use (with filters) and when to avoid (broad queries), addressing performance concerns directly.

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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