DART-MCP

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

With no annotations provided, the description carries full burden for behavioral disclosure but only minimally indicates it returns '텍스트' (text) versus structured data. It fails to disclose data source, rate limits, authentication requirements, caching behavior, or error scenarios that an agent would need to handle this safely.

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. While the information_type enumeration is lengthy, every line is necessary given the lack of schema enums. The front-loaded purpose statement followed by detailed parameter documentation represents effective information architecture.

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?

For a 4-parameter tool with no output schema and 0% parameter coverage, the description adequately covers all parameters including format constraints and categorical options. It indicates the return type (text). Minor gaps include lack of error handling documentation and date range constraints (e.g., maximum range limits).

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 description excellently compensates by providing detailed Args documentation: date format specifications (YYYYMMDD with examples), company name examples (삼성전자, 네이버), and comprehensive enum-like explanations for information_type with 7 distinct categories and their meanings. This adds critical semantic value missing from 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 it provides '회사의 사업 관련 현황 정보' (company business-related status information) with specific verb and resource. It implicitly distinguishes from siblings like search_detailed_financial_data (financial vs. business operational data) and search_json_financial_data (text return vs. JSON), though explicit differentiation from search_disclosure would strengthen this further.

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 implied usage guidance through the detailed enumeration of information_type categories (e.g., when to request '매출 및 수주상황' vs '주요계약 및 연구개발활동'), but lacks explicit when-to-use guidance relative to sibling tools like search_disclosure or search_detailed_financial_data.

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