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twjackysu

TWSE MCP Server

get_stock_yearly_trading

Retrieve yearly trading data for Taiwan-listed stocks using stock codes. Provides annual volume, value, price ranges, and transaction statistics for investment analysis.

Instructions

Obtain yearly trading information for a listed company stock based on its stock code.

Returns information including:

  • Year: Trading year (ROC calendar YYY)

  • Code: Stock code

  • Name: Stock name

  • TradeVolume: Total trade volume (in shares)

  • TradeValue: Total trade value (in TWD)

  • Transaction: Total transaction count

  • HighestPrice: Highest price in the year

  • HDate: Date of highest price

  • LowestPrice: Lowest price in the year

  • LDate: Date of lowest price

  • AvgClosingPrice: Average closing price

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns specific yearly trading data, which is useful, but it doesn't mention behavioral traits like whether it's read-only (implied by 'Obtain'), potential rate limits, error handling, or data freshness. The description adds basic context but lacks depth for a tool with no annotations.

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 well-structured and front-loaded with the main purpose in the first sentence, followed by a clear bulleted list of return values. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

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 the tool's low complexity (1 parameter), no annotations, and the presence of an output schema (implied by the detailed return list), the description is mostly complete. It explains what the tool does and what it returns, but it could improve by adding usage context or behavioral details to fully guide the agent in a rich sibling environment.

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 input schema has 1 parameter with 0% description coverage, but the description clarifies that 'code' refers to a 'stock code' for a 'listed company stock,' adding meaningful semantics beyond the schema. However, it doesn't specify format details (e.g., length, allowed characters), so it doesn't fully compensate but provides good value.

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's purpose with a specific verb ('Obtain') and resource ('yearly trading information for a listed company stock'), and it distinguishes itself from siblings like 'get_stock_daily_trading' or 'get_stock_monthly_trading' by specifying the yearly timeframe, making it easy to differentiate.

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

Usage Guidelines3/5

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

The description implies usage by stating it's for 'yearly trading information,' which suggests it should be used when annual data is needed, but it doesn't explicitly say when to use this tool versus alternatives (e.g., daily or monthly tools) or provide any exclusions. This leaves some ambiguity for the agent in selecting among similar tools.

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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