get_sales_contract_disclosures

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

With no annotations provided, the description carries the behavioral disclosure burden. It explicitly states that each filing's original document is downloaded and parsed because there is no structured JSON API, and it lists the extracted fields (contract amount, period, counterparty). This gives material behavioral insight beyond the tool name and schema.

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 well-organized: a one-line summary, a purpose/behavior paragraph, and a clean Args block. Every sentence adds information—the Korean disclosure type, the extracted data, the lack of structured API, and clear parameter semantics—without fluff.

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 covers the tool's purpose, input semantics, behavior, and why downloading/parsing is necessary. Since an output schema exists, the description does not need to explain return values. Minor gaps such as pagination, rate limits, or potential cost of reading many filings are not mentioned, but the available context is strong enough for correct invocation.

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?

The input schema provides only type and title for each parameter, with 0% description coverage. The description compensates fully by explaining each parameter: corp_code is the eight-digit Open DART code with a concrete example, and start_date/end_date are defined as filing-date range boundaries with accepted formats YYYYMMDD or YYYY-MM-DD.

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 uses a specific verb and resource: 'Get single sale/supply contract disclosures' and then expands with 'Finds every 단일판매ㆍ공급계약체결 filing for a company in a date range.' This clearly distinguishes it from sibling tools like search_disclosures or get_disclosure_document_text by identifying both the exact disclosure type and the extraction behavior.

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 gives clear context: it is used for tracking order backlog from disclosed sales contracts and notes that this disclosure type has no structured JSON API, which explains why document parsing is needed. It does not explicitly name alternatives or exclusion conditions, but the context is strong enough for an agent to infer when this tool is appropriate.

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