AgentLadle MCP DART

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A MCP (Model Context Protocol) server that provides tools for discovering, downloading, parsing, and searching Korean DART financial reports (금융감독원 전자공시시스템).

It enables AI assistants (Claude, Cursor, etc.) to access Korea's Open DART data through 6 structured tools — from resolving a company name to keyword-searching within report pages.

Features

• 6 MCP tools for DART data: resolve company name, list filings, download+parse, get TOC, read pages, keyword search

• Full 정기공시 financial report support — A001 사업보고서 (annual), A002 반기보고서 (semi-annual), A003 분기보고서 (quarterly), each with a dedicated toc.yaml section mapping derived from real DART XML

• Auto-detecting multi-format parser — structured SECTION-N XML (A/B/D/E types) routes to the section-tree parser (toc.yaml when available, generic tree extraction otherwise); HTML single-page disclosures (I001 수시공시, I002 공정공시/잠정실적) route to the HTML extraction parser. Format is detected by file content, not hardcoded by type.

• Professional DART document parsing — direct XML path extraction (./P, ./TABLE) and standard TOC alignment (A001: 123 codes / 110 leaves; A002: 53 codes / 43 leaves; A003: 59 codes / 48 leaves)

• 章节树 + 节内限页 pagination model — pages respect DART's standard section tree (precision over fixed 4000-char chunking)

• Korean-aware search — substring matching (no \b word boundaries), character-count TF normalization, morphological-variant hints

• Three-tier local cache — ZIP archives, extracted XML, and parsed JSON stored separately under ~/.agentladle/mcp-dart/data/{zip,xml,json}/

• Idempotent — already-downloaded/parsed filings are automatically skipped

• Pure Python, cross-platform (Windows / macOS / Linux)

Related MCP server: MCP OpenDART

Prerequisites

• Python 3.10+ — Download Python

• uv — Install uv

• DART API key (free) — register at https://opendart.fss.or.kr/

Note: After installing uv, restart your terminal and MCP client (e.g. Cherry Studio) to ensure the uv command is recognized.

Quick Start

Add to your MCP client configuration (Claude Desktop, Cursor, etc.):

{
  "mcpServers": {
    "mcp-dart": {
      "command": "uvx",
      "args": ["agentladle-mcp-dart"],
      "env": {
        "DART_API_KEY": "your_dart_api_key_here",
        "UV_HTTP_TIMEOUT": "300"
      }
    }
  }
}

That's it. uvx will automatically download the package and its dependencies from PyPI — no clone, no manual install, no path configuration.

Slow network? The first uvx run downloads many dependencies (including dart-fss, pandas, etc.). The default 30s timeout may be too short and cause MCP Connection closed. Set UV_HTTP_TIMEOUT to "300" to avoid download timeouts. If it still fails, use the pip install alternative below.

Alternative: .env file

If you prefer not to inject the key via the MCP client env block, copy .env.example to one of:

• ./.env (per-project override; git-ignored — never commit a real key)

• ~/.agentladle/mcp-dart/.env (user-global default)

and set:

DART_API_KEY=your_dart_api_key_here

The first existing .env wins; explicit env vars set in the MCP client always override .env. See .env.example for details.

Alternative: pip install

If you prefer managing the environment yourself:

pip install agentladle-mcp-dart

Then configure (no need for uvx):

{
  "mcpServers": {
    "mcp-dart": {
      "command": "agentladle-mcp-dart",
      "env": { "DART_API_KEY": "your_dart_api_key_here" }
    }
  }
}

Alternative: Run from source (local development)

Clone the repository and run directly:

git clone https://github.com/agentladle/mcp-dart.git

Then configure your MCP client:

{
  "mcpServers": {
    "mcp-dart": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/mcp-dart", "agentladle-mcp-dart"],
      "env": { "DART_API_KEY": "your_dart_api_key_here" }
    }
  }
}

Replace /path/to/mcp-dart with the actual path to the cloned repository.

Data Flow

DART OpenAPI                      Local Files (~/.agentladle/mcp-dart/data/)
────────────                      ──────────────────────────────────────────
corp_list (dart-fss)  ──→         corp_list.csv                (CSV cache, ~114k corps)
search_dart_company    ──→        corp_list.csv lookup         (Tool 6: name → stock_code)
                                     │
search_filings API     ──→        zip/{rcept_no}.zip           (Tool 2: download)
                                     │
ZIP extraction         ──→        xml/{rcept_no}/*.xml           (Tool 2: extract)
                                     │
dart_parsers + toc.yaml ──→       json/{stock_code}_{rcept_no}.json  (Tool 2: parse)
                                     │
Local TF search        ──→        search results               (Tool 5: keyword_search)
TOC (section_tree)     ──→        section_tree + page ranges   (Tool 3: get_report_toc)
Page range read        ──→        page content                 (Tool 4: get_report_pages)

Tools

Tool 1: list_dart_filings

List available DART filings for a Korean listed company.

Returns each filing's rcept_no, rcept_dt, report_nm, corp_code, report_type, and a parseable flag (true for any valid DART type — the parser auto-detects document format at parse time).

Tool 2: download_dart_report

Download and parse a single DART filing. Fuses the SEC flow's download + parse into one step. Idempotent (skips if cached and valid).

Tool 3: get_report_toc

Retrieve the complete DART section_tree (Table of Contents) for a parsed report. Built directly from toc.yaml aligned with the parsed XML — page ranges are authoritative, not heuristic.

Each node has section_code, title, start_page, end_page, local_pages, matched (bool — whether XML matched this toc entry), and children. Pass any section_code to Tool 4's section_code parameter to read that whole subtree.

Tool 4: get_report_pages

Read full page content by global page range or by section_code.

Tool 5: keyword_search

Korean-friendly full-text search. Scoring:

• TF = substring count / non-whitespace character count (Korean has no whitespace-delimited words)

• Position boost ×1.2 if first hit is in the top 20% of the page

• ALL match mode applies ×2.0 bonus when every keyword hits

Each match returns page_number, score, keyword_hits, snippet (highlighted with **...**), and the section context (section_code/section_title).

Tool 6: search_dart_company

Resolve a company name (Korean or English) to a stock_code / corp_code. Queries the locally cached corp_list.csv (no network call after first preheat). Use this before list_dart_filings / download_dart_report whenever the user mentions a company by name but does not provide a 6-digit stock_code.

Each match contains corp_name, corp_eng_name, stock_code, corp_code, modify_date. When multiple matches are returned, pick the correct stock_code and pass it to list_dart_filings.

Configuration

After first run, a default config file is created at ~/.agentladle/mcp-dart/config.yaml:

dart:
  api_key: ""

paths:
  data_dir: "~/.agentladle/mcp-dart/data"
  zip_dir: "~/.agentladle/mcp-dart/data/zip"
  xml_dir: "~/.agentladle/mcp-dart/data/xml"
  json_dir: "~/.agentladle/mcp-dart/data/json"

parsing:
  page_char_limit: 4000
  max_pages_per_section: 10      # soft target (precision preserved on overflow)

download:
  delay_between_requests: 0.2

DART_API_KEY resolution priority (highest to lowest):

1. Real OS environment variable (DART_API_KEY=xxx uvx agentladle-mcp-dart)

2. .env file — ./.env first, then ~/.agentladle/mcp-dart/.env

3. dart.api_key in ~/.agentladle/mcp-dart/config.yaml

Data Directory Structure

~/.agentladle/mcp-dart/
├── .env                              # Optional user-global API key (git-ignored)
├── config.yaml                       # Configuration (auto-created)
└── data/
    ├── corp_list.csv                 # ~114k Korean companies (CSV cache, dart-fss)
    ├── zip/
    │   └── {rcept_no}.zip            # Original DART archive (retained after download)
    ├── xml/
    │   └── {rcept_no}/               # Extracted XML per filing
    │       ├── {rcept_no}.xml        # Main DART XML
    │       └── {rcept_no}_NNNNN.xml  # Optional attachments
    └── json/
        └── {stock_code}_{rcept_no}.json   # Parsed section_tree + pages + coverage

File naming convention: {stock_code}_{rcept_no}.json when stock_code is known; {rcept_no}.json when stock_code was omitted at download time. find_json_file also falls back to *_{rcept_no}.json glob and legacy raw/ / xml/ co-located layouts.

Example Usage

The tools follow an EAFP (Easier to Ask for Forgiveness than Permission) approach. AI assistants should attempt to read/search directly and rely on errors to trigger downloads.

Scenario A: File already exists locally (Shortest Path)

User: "Analyze Samsung's latest financial report."

1. keyword_search(rcept_no="<rcept_no>", keywords=["매출", "매출액", "영업이익"])
   → Returns page snippets matching the keywords immediately.

Scenario B: File missing (Fallback triggered)

User: "What does LG Energy Solution's latest annual report say about R&D?"

1. keyword_search(rcept_no="<rcept_no>", keywords=["연구개발", "R&D"])
   → Error: Parsed report not found.
2. list_dart_filings(stock_code="373220", report_types=["A001"])
   → Returns the correct rcept_no.
3. download_dart_report(rcept_no="<rcept_no>")
   → Downloads ZIP, extracts XMLs, parses to JSON cache.
4. keyword_search(rcept_no="<rcept_no>", keywords=["연구개발", "R&D"])
   → Now returns hits with section context.

Scenario C: Latest ad-hoc disclosure (Samsung earnings guidance / 잠정실적)

User: "Analyze Samsung's latest earnings guidance."

1. list_dart_filings(stock_code="005930", report_types=["I002"], limit=1)
   → Returns the latest 공정공시 (e.g. 잠정실적 / provisional earnings).
2. download_dart_report(rcept_no="<rcept_no>", stock_code="005930", report_type="I002")
   → Parses the HTML single-page disclosure.
3. keyword_search(rcept_no="<rcept_no>", keywords=["매출", "영업이익", "실적"])
   → AI summarizes revenue, operating profit, and YoY change.

Tech Stack

License

MIT