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twjackysu

TWSE MCP Server

get_company_profitability_analysis

Analyze company profitability using stock codes to assess financial performance metrics for listed companies on the Taiwan Stock Exchange.

Instructions

Get profitability analysis for a specific listed company based on its stock code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior1/5

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 only states what the tool does, without mentioning any behavioral traits like data freshness, rate limits, authentication requirements, error conditions, or what the output contains. This leaves the agent with insufficient information to use the tool effectively.

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 a single, well-structured sentence that efficiently conveys the core purpose without unnecessary words. It's appropriately sized for a simple tool and front-loads the key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (financial analysis tool with no annotations) and the presence of an output schema (which helps), the description is incomplete. It lacks behavioral context, usage differentiation, and sufficient parameter guidance, making it inadequate for an agent to confidently invoke the tool without additional assumptions.

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?

The description adds minimal semantics beyond the input schema. It specifies that the 'code' parameter is a 'stock code' for a 'listed company', which provides context not in the schema (which has 0% description coverage). However, it doesn't clarify format (e.g., ticker symbol, ISIN), validation rules, or examples, leaving gaps in parameter understanding.

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 action ('Get profitability analysis') and target resource ('for a specific listed company based on its stock code'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'get_company_profitability_analysis_summary' or other company-specific tools, which prevents a perfect score.

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. With many sibling tools focused on company data (e.g., 'get_company_balance_sheet', 'get_company_income_statement'), there's no indication of how profitability analysis differs or when it's preferred over other financial 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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