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twjackysu

TWSE MCP Server

get_public_company_monthly_revenue

Retrieve monthly revenue data for public companies using their stock code to analyze financial performance trends.

Instructions

Obtain monthly revenue summary for a public company based on its stock code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Obtain') but lacks details on data sources, update frequency, rate limits, authentication needs, or error handling. For a tool fetching financial data, this omission is significant, as it doesn't inform the agent about reliability or operational constraints.

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, efficient sentence that front-loads the core purpose without unnecessary words. It directly communicates what the tool does and the required input, making it highly concise and well-structured for quick comprehension.

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

Completeness3/5

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

Given the tool's complexity (financial data retrieval), lack of annotations, and 0% schema description coverage, the description is minimally adequate. It states the purpose and parameter but misses behavioral context and detailed usage guidelines. The presence of an output schema helps by defining return values, but overall completeness is limited, scoring at the baseline for a simple tool with gaps.

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 schema description coverage is 0%, so the description must compensate. It specifies that the parameter 'code' is a 'stock code', adding semantic meaning beyond the schema's generic 'Code' title. However, it doesn't clarify format (e.g., ticker symbol conventions), examples, or validation rules, 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 verb ('Obtain') and resource ('monthly revenue summary for a public company'), making the purpose specific and understandable. However, it does not explicitly differentiate from its sibling 'get_company_monthly_revenue', which might serve a similar purpose but potentially for different company types or data sources, leaving room for ambiguity.

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, such as the sibling 'get_company_monthly_revenue' or other financial data tools. It mentions the input ('stock code') but offers no context on prerequisites, limitations, or comparative use cases, leaving the agent without clear usage directives.

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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