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TWSE MCP Server

TWSE MCP Server

by twjackysu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_stock_daily_trading

Retrieve daily stock trading data from the Taiwan Stock Exchange by entering a stock code. Get trading volume, price changes, opening/closing prices, and transaction details for analysis.

Instructions

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

Returns information including:

  • Date: Trading date (ROC calendar YYYMMDD)

  • Code: Stock code

  • Name: Stock name

  • TradeVolume: Trade volume (in shares)

  • TradeValue: Trade value (in TWD)

  • OpeningPrice: Opening price

  • HighestPrice: Highest price

  • LowestPrice: Lowest price

  • ClosingPrice: Closing price

  • Change: Price change

  • Transaction: Transaction count

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "code": "<code>"
    },
    "name": "get_stock_daily_trading"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
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 of behavioral disclosure. It describes the return format in detail (listing specific fields like date, prices, and volumes), which is helpful. However, it does not mention potential limitations such as data availability, rate limits, or error handling, leaving gaps in behavioral context 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 concise: the first sentence states the purpose, followed by a bulleted list of return fields. Every sentence earns its place, with no redundant or verbose language, making it easy to parse and understand quickly.

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 complexity (simple query with one parameter), the description is reasonably complete. It details the return fields comprehensively, and since an output schema exists, it need not explain return values further. However, it lacks usage guidelines and behavioral details like error conditions, which slightly reduces completeness for a tool with no annotations.

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 compensates by specifying that the tool operates 'based on its stock code,' clarifying the parameter's purpose. It does not provide format details (e.g., code length or examples), but given the single parameter and the description's clarification, it adds meaningful value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Obtain daily trading information for a listed company stock based on its stock code.' It specifies the verb ('obtain'), resource ('daily trading information'), and scope ('listed company stock'), making it easy to understand what the tool does. However, it does not explicitly differentiate from sibling tools like 'get_stock_monthly_trading' or 'get_stock_yearly_trading', which prevents a score of 5.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It mentions the input parameter ('stock code') but does not specify prerequisites, constraints, or recommend other tools for related tasks (e.g., monthly or yearly data). This lack of contextual usage advice limits its effectiveness for an AI agent.

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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