DART-MCP

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

With no annotations provided, the description carries the full disclosure burden. It successfully documents the data source method (XBRL parsing) and return format (text), but fails to mention critical behavioral traits like read-only status, rate limits, error handling scenarios (e.g., invalid company names), or data availability constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description uses a clear Args/Returns structure that efficiently organizes information. Given the lack of schema descriptions and output schema, the length is appropriate and necessary. No sentences are wasted, though the Korean text is slightly more verbose than the minimal English equivalent.

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

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

The description adequately covers input parameters and return values (compensating for missing output schema), but remains incomplete regarding operational context. For a financial data tool of this complexity, it should mention data freshness, potential for missing filings, or authentication requirements, especially absent any annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema description coverage, the Args section fully compensates by providing comprehensive semantics: date format specifications (YYYYMMDD), statement_type enum values ("재무상태표", "손익계산서", "현금흐름표"), default behavior for None, and concrete examples for company names. This adds substantial value beyond the schema.

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

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

The description clearly states the tool parses XBRL files to extract detailed financial data (specific verb + resource). However, it does not explicitly differentiate when to use this versus the sibling 'search_json_financial_data' tool, though it implies distinction via the XBRL and text-output mentions.

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

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 like 'search_json_financial_data' or 'search_business_information'. It lacks explicit when-to-use or when-not-to-use conditions, prerequisites (e.g., specific company naming conventions), or exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.