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 full burden of behavioral disclosure. It transparently states that the tool downloads and parses original filing documents, scans every relevant filing in the date range, and extracts specific fields. This makes it clear that the operation is read-only and non-mutating. It does not mention rate limits or error behavior, but the core behavioral traits are disclosed.

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-structured: a one-line summary, a concise behavioral explanation, and clear parameter documentation. It includes useful context about the disclosure type and why document parsing is required. There is minor redundancy between 'reads each one to extract' and 'each filing's original document is downloaded and parsed,' but overall every section earns its place.

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 is complete for a three-parameter tool with an output schema: it explains the domain, the filtering scope, the extracted fields, the reason for document parsing, and all parameters. It does not discuss pagination, rate limits, or no-result scenarios, which would be useful but are not critical given the output schema and the description's overall clarity.

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?

Schema description coverage is 0%, but the description fully compensates by documenting all three parameters. It explains corp_code as the company's eight-digit Open DART code with a concrete example (Samsung Electronics is 00126380), and specifies start_date and end_date as filing-date search range boundaries with accepted formats YYYYMMDD or YYYY-MM-DD. This adds meaning well beyond the bare 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 uses a specific verb and resource: 'Get single sale/supply contract disclosures' and explains exactly what it does—finds every 단일판매ㆍ공급계약체결 filing for a company in a date range and extracts contract amount, period, counterparty, and related details. This clearly distinguishes it from generic sibling tools like search_disclosures or get_disclosure_document_text by focusing on a specific disclosure type and an aggregated extraction purpose.

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 for when to use the tool: tracking order backlog (수주잔고) from disclosed sales contracts in a date range. It also explains why this specific tool is needed—there is no structured JSON API for this disclosure type in Open DART, so it downloads and parses original documents. It does not explicitly name alternatives or state when not to use it, but the context is clear enough for an agent to select it appropriately.

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