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TWSE MCP Server

TWSE MCP Server

by twjackysu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_stock_valuation_ratios

Calculate key stock valuation metrics including P/E ratio, dividend yield, and P/B ratio for Taiwan-listed companies using their stock codes.

Instructions

Obtain P/E ratio, dividend yield, and P/B ratio for a listed company stock based on its stock code.

Returns information including:

  • Date: Data date (ROC calendar YYYMMDD)

  • Code: Stock code

  • Name: Stock name

  • PEratio: Price-to-Earnings ratio

  • DividendYield: Dividend yield (%)

  • PBratio: Price-to-Book ratio

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_valuation_ratios"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYes

Output 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?

With no annotations provided, the description carries the full burden. It discloses the return format (including fields like Date, Code, Name, etc.), which is helpful. However, it doesn't mention behavioral traits such as data freshness (e.g., real-time vs. historical), rate limits, error handling, or authentication needs, leaving gaps in transparency for a data 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?

The description is well-structured and front-loaded, starting with the core purpose and followed by a clear bulleted list of return fields. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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) and the presence of an output schema (implied by the detailed return description), the description is mostly complete. It covers the purpose, parameter meaning, and return structure adequately. However, it lacks context on data sources or limitations, which could be useful for full completeness.

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 0% description coverage, but the description compensates by explaining that the 'code' parameter is a 'stock code' for a 'listed company stock.' This adds meaningful context beyond the schema's basic type definition, though it doesn't specify format constraints (e.g., length, pattern) or provide examples.

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 specific action ('Obtain') and resources ('P/E ratio, dividend yield, and P/B ratio for a listed company stock'), distinguishing it from siblings that focus on different financial metrics, trading data, or company information. It precisely identifies the three valuation ratios returned, making the purpose unambiguous.

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 specifying 'based on its stock code,' which suggests it's for retrieving valuation data for a specific stock. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_valuation_ratios_by_date' or other financial data tools, leaving room for ambiguity in sibling differentiation.

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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