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alekskram

arcus-agent-gateway

Corporate Actions

corporate_actions
Read-only

Get corporate actions (splits, dividends) across all tokens or filtered to one symbol. Each record shows type, status, process date, old/new rates, and original raw data.

Instructions

Corporate actions (splits, dividends) across all tokens, or filtered to one symbol. Each row: symbol, type, status, process_date, details {old_rate, new_rate} (floats when the source carries rate fields) and raw (the original record, untouched). Tolerant to the API's field-name variants. Example: corporate_actions(symbol="AAPL", limit=5)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
symbolNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.2.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds valuable behavioral details: tolerance to API field-name variants, the exact row structure (symbol, type, status, process_date, details, raw), and the note that rates are floats when present. It also clarifies 'raw' is untouched original data. This goes beyond simple schema repetition, though it doesn't address rate limits or potential data-volume 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?

Compact but information-dense: purpose, filter behavior, row schema, type caveat, tolerance, and an example fit into three sentences. Front-loaded with the main action and scope, then builds detail efficiently. No 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?

Given an output schema exists, the description needn't restate return values—it complements it by clarifying row semantics, tolerance, and the optional filter. With only 2 optional params and strong annotations, an agent has everything needed to select and invoke this tool correctly against sibling tools.

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?

Schema coverage is 0%, so the description must carry the weight. It explains the two params via the example (symbol and limit) and the scope statement: symbol filters to one token, omitting it means all tokens. This sufficiently compensates for the bare schema, even though it doesn't formally document every property.

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?

States a specific verb+resource: retrieves corporate actions (splits, dividends) across all tokens or one symbol. Highlights row fields and example usage, and differs from siblings like quotes/price_history by focusing on corporate action events. The scoping and output row composition make it unambiguous.

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?

Explicitly clarifies the tool works across all tokens or filtered by one symbol, and the example demonstrates both the optional filter and limit. It does not explicitly contrast against siblings (e.g., price_history for price data), but the domain-specific scope and row description make the appropriate context clear. Minor gap: no explicit 'use this when' statement.

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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