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OldTemple91

koreafilings-mcp

list_recent_filings

Retrieve recent DART filings from Korean companies to identify which disclosures worth paying for. Returns metadata including rcpt_no and ticker, enabling informed decisions on paid summary calls.

Instructions

Browse recent DART filings across every listed Korean company. Free.

Returns metadata only — no AI summaries — so an agent can decide
which filings warrant a paid call. Each entry includes ``rcpt_no``
(for ``get_disclosure_summary``) and ``ticker`` (for
``get_recent_filings``).

Args:
    limit: Max filings to return (1-100, default 20).
    since_hours: Look back this many hours (1-168, default 24).

Returns:
    A list of filing-metadata dicts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
since_hoursNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler for 'list_recent_filings'. Decorated with @mcp.tool(), it creates a temporary Client (with a dummy private key), calls probe.list_recent_filings(), and maps the result to a list of dicts with rcpt_no, ticker, corp_name, report_nm, rcept_dt, dart_url.
    @mcp.tool()
    def list_recent_filings(limit: int = 20, since_hours: int = 24) -> list[dict[str, Any]]:
        """Browse recent DART filings across every listed Korean company. Free.
    
        Returns metadata only — no AI summaries — so an agent can decide
        which filings warrant a paid call. Each entry includes ``rcpt_no``
        (for ``get_disclosure_summary``) and ``ticker`` (for
        ``get_recent_filings``).
    
        Args:
            limit: Max filings to return (1-100, default 20).
            since_hours: Look back this many hours (1-168, default 24).
    
        Returns:
            A list of filing-metadata dicts.
        """
        network = os.environ.get("KOREAFILINGS_NETWORK", "base-sepolia").strip() or "base-sepolia"
        base_url = os.environ.get("KOREAFILINGS_BASE_URL", "https://api.koreafilings.com").strip()
        probe = Client(private_key="0x" + "00" * 32, network=network, base_url=base_url)
        try:
            filings = probe.list_recent_filings(limit=limit, since_hours=since_hours)
        finally:
            probe.close()
        return [
            {
                "rcpt_no": f.rcpt_no,
                "ticker": f.ticker,
                "corp_name": f.corp_name,
                "report_nm": f.report_nm,
                "rcept_dt": f.rcept_dt.isoformat(),
                "dart_url": f"https://dart.fss.or.kr/dsaf001/main.do?rcpNo={f.rcpt_no}",
            }
            for f in filings
        ]
  • Registration of the 'list_recent_filings' tool via the @mcp.tool() decorator on line 134 in the FastMCP server instance.
    @mcp.tool()
  • SDK Client.list_recent_filings method — makes an HTTP GET to /v1/disclosures/recent with limit/since_hours params, returns List[RecentFiling] parsed from the response JSON.
    def list_recent_filings(
            self,
            limit: int = 20,
            since_hours: int = 24,
    ) -> List[RecentFiling]:
        """Browse recent DART filings across every listed company. Free.
    
        Returns metadata only — no AI summaries. Use this to discover
        what is happening today, then call :meth:`get_recent_filings`
        or :meth:`get_summary` to pay for the summaries you actually
        want.
        """
        resp = self._http.get(
            f"{self._base_url}/v1/disclosures/recent",
            params={
                "limit": min(max(limit, 1), 100),
                "since_hours": min(max(since_hours, 1), 168),
            },
        )
        if resp.status_code != 200:
            raise ApiError(resp.status_code, _safe_json(resp))
        body = resp.json() or {}
        return [RecentFiling.model_validate(f) for f in body.get("filings", [])]
  • RecentFiling Pydantic model — defines the schema for each filing returned by list_recent_filings, including rcpt_no (alias rcptNo), ticker, corp_name, report_nm, rcept_dt.
    class RecentFiling(BaseModel):
        """Lightweight metadata for a recent DART filing — no AI summary.
    
        Returned by the free :meth:`Client.list_recent_filings`. Use the
        ``rcpt_no`` to fetch a single summary via :meth:`Client.get_summary`,
        or the ``ticker`` to fetch a batch via
        :meth:`Client.get_recent_filings`.
        """
    
        model_config = ConfigDict(populate_by_name=True, frozen=True)
    
        rcpt_no: str = Field(alias="rcptNo")
        ticker: Optional[str] = None
        corp_name: str = Field(alias="corpName")
        report_nm: str = Field(alias="reportNm")
        rcept_dt: date = Field(alias="rceptDt")
Behavior4/5

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

No annotations are provided, so the description carries full burden. It states 'Returns metadata only' implying read-only behavior and mentions 'Free', but it does not explicitly confirm safety, idempotency, or authentication requirements. The description is mostly adequate but lacks explicit transparency on side effects.

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

Conciseness5/5

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

The description is concise (6 sentences), well-structured with clear sections (purpose, return type, args, returns), and front-loads the primary purpose. Every sentence earns its place, with no fluff.

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 the tool's simplicity (2 optional parameters) and the presence of an output schema, the description is adequately complete. It covers metadata-only return and cross-references other tools, providing sufficient context for an agent to use this tool correctly.

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 description fully documents both parameters (limit and since_hours) with ranges and defaults, compensating for 0% schema description coverage. This adds meaning beyond the bare input schema, enabling precise agent decisions.

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

Purpose4/5

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

The description clearly states that the tool browses recent DART filings for Korean companies and returns metadata only. However, it does not differentiate itself from the sibling tool 'get_recent_filings', which has a similar name and purpose, creating potential ambiguity.

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 hints at a usage flow by referencing get_disclosure_summary for paid calls, but it does not explicitly state when to use this tool versus alternatives like get_recent_filings. It provides some context without clear when-to-use or when-not-to-use guidance.

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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