dart-risk-mcp
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- AlicenseNot gradedqualityBmaintenanceEnables AI agents to access Korea's DART financial data system for retrieving and analyzing corporate disclosures and financial information through natural language queries.401 npmMIT
- AlicenseBqualityDmaintenanceProvides natural language access to South Korean corporate disclosure data, financial statements, and shareholder information through the DART Open API. It enables users to query 83 different tools for real-time reporting and regulatory filings from Korean listed companies.8333 PyPI3MIT
- AlicenseNot gradedqualityDmaintenanceProvides AI assistants with real-time access to Korean listed companies' disclosures, financial statements, and corporate information via the DART API.4MIT
TDQS
Scored across 34 tools
Most tools target distinct data domains and the descriptions are unusually explicit about boundaries. However, several close pairs exist (get_disclosure_document vs view_disclosure, get_financial_summary vs get_financial_statements_full, check_disclosure_risk vs check_disclosure_anomaly), so an agent could easily misroute a retrieval.
Nearly all tools follow a snake_case verb_noun pattern, with check_/track_/get_/list_/find_ verbs mapping sensibly to actions. Minor inconsistencies remain, such as get_disclosure_document vs view_disclosure both reading document content, and prepositional names like list_disclosures_by_stock and search_notes_in_report.
34 tools is firmly in the 'too many' range for a single MCP. Document reading is split across three tools, financial statements across three tools, and audit opinions across two tools, creating fragmentation that could be consolidated without losing functionality.
The tool surface covers the full investigative loop: disclosure discovery, document inspection, financial/audit/ownership data, risk signals, actor overlap, watchlists, and market context. The main gap is that market-wide discovery is limited to fixed presets rather than arbitrary keyword search across all filings.