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OldTemple91

koreafilings-mcp

get_disclosure_summary

Fetch an AI-generated English summary of a Korean DART disclosure. Returns summary, importance score 1–10, event type, sector tags, and payment proof. Pay per call in USDC via x402 on Base.

Instructions

Fetch the AI-generated English summary of a Korean DART disclosure.

**This tool spends real USDC from the configured wallet** — 0.005
USDC per call as of v0.1, settled on-chain via x402. The wallet
pays only on a successful 200 response; 4xx/5xx failures do not
settle.

Args:
    rcpt_no: 14-digit DART receipt number, e.g. ``"20260424900874"``.
        You can discover receipt numbers from the DART portal at
        https://dart.fss.or.kr/ or from koreafilings.com's listing
        endpoints as they come online.

Returns:
    A dict with the summary content (``summary_en``), operational
    metadata (``importance_score`` 1–10, ``event_type``,
    ``ticker_tags``, ``sector_tags``, ``actionable_for``,
    ``generated_at``), and payment proof (``paid_tx``, ``network``,
    ``payer``). If the server served from its free-tier path the
    payment block is absent.

Raises:
    RuntimeError: when the SDK rejects the request. The message
        distinguishes payment failures (facilitator rejection,
        network mismatch, insufficient balance) from other API
        errors (404 unknown rcpt_no, 429 rate limit, 5xx upstream).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rcpt_noYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses the real USDC cost, settlement conditions, error handling, and return value structure including payment proof. This exceeds expectations for transparency.

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

Conciseness4/5

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

The description is well-structured with sections and front-loaded with purpose and cost warning. It is somewhat lengthy but every sentence serves a clear purpose, earning a 4.

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

Completeness5/5

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

Given one parameter and an output schema, the description covers input, output structure, errors, cost, and use case. It is complete and leaves no gaps for the agent.

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

Parameters5/5

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

The single parameter rcpt_no is documented with a 14-digit format, an example, and sources for discovery. Schema description coverage is 0%, but the description compensates fully, adding significant meaning.

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

Purpose5/5

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

The description clearly states the tool fetches an AI-generated English summary of a Korean DART disclosure. It specifies the resource (disclosure summary) and action (fetch), and is distinct from sibling tools like find_company or get_pricing.

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use (need a summary), provides cost and failure details, and tells how to discover receipt numbers. It lacks explicit when-not or alternative tools, but the context is sufficiently clear.

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

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