OpenDART MCP Server

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. It only states what data is provided without mentioning whether this is a read-only operation, if there are rate limits, authentication requirements, or what format the output takes. The description doesn't contradict annotations (since none exist), but provides minimal behavioral context for a tool that presumably accesses financial data.

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 is appropriately sized with a clear purpose statement followed by parameter documentation. However, the structure could be improved by front-loading more critical information about the tool's behavior and usage context. The parameter documentation is thorough but occupies most of the description space.

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?

Given the tool has an output schema (which handles return values) and the description documents all parameters, the description is minimally complete. However, for a financial data tool with no annotations, it should provide more context about data freshness, source reliability, and typical use cases to be fully helpful to an AI agent.

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?

With 0% schema description coverage, the description compensates well by documenting all 3 parameters in the Args section with clear explanations: corp_code (8-digit unique number), bsns_year (business year, 4 digits, 2015+), and reprt_code (report codes with specific values). This adds substantial meaning beyond the bare schema, though it doesn't explain parameter constraints or validation rules.

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's purpose: '공모자금의 사용내역 - 정기보고서 내 공모자금의 사용내역 정보를 제공합니다' (Provides public offering fund usage details from periodic reports). It specifies the resource (public offering funds) and the information provided (usage details from reports), but doesn't explicitly differentiate from sibling tools like 'get_private_placement_fund_usage' beyond the 'public' vs 'private' distinction in names.

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. While it mentions the data source (periodic reports), it doesn't indicate when this tool is appropriate compared to other financial data tools in the sibling list, nor does it mention prerequisites or exclusions for usage.

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