DataSinking

DataSinking

Full-text financial reports across the US and Asia, as clean Markdown.

DataSinking serves full-text financial reports — annual, semi-annual and quarterly — from the US, China, Japan, Korea and Taiwan as clean Markdown, ready for LLM reading and RAG. Query by FMP-style symbol (AAPL, 600519.SS, 7203.T, 005930.KS, 2330.TW) or filter by exchange, report period, or section — pull just the MD&A / risk section instead of the whole filing, saving tokens. Every figure is traceable to its source disclosure and block. Reports are sourced from official disclosure platforms and parsed into structured Markdown with YAML frontmatter, preserved headings, paragraphs and tables.

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MCP server

Ship DataSinking to any AI agent (Claude / Cursor / Codex / Devin Desktop) as an MCP server — 6 tools: list exchanges, list stocks, list reports, fetch a report, list sections, fetch one section (token-friendly for RAG).

Hosted — nothing to install

One URL, no package, no Python, no local server. Most clients take this shape:

{
  "mcpServers": {
    "datasinking": {
      "type": "http",
      "url": "https://api.datasink.ing/mcp",
      "headers": { "Authorization": "Bearer YOUR_KEY" }
    }
  }
}

Claude Code, in one line:

claude mcp add --transport http datasinking https://api.datasink.ing/mcp \
  --header "Authorization: Bearer YOUR_KEY"

A client that can't set a header can put the key in the URL instead — https://api.datasink.ing/mcp?apikey=YOUR_KEY — but only do that when you must: a key in a URL ends up in logs and screen shares, and no client can redact it.

Two clients don't fit the shape above: Codex's config is TOML, and Devin Desktop's remote field is serverUrl, not url. Per-client files: docs/mcp/.

Local — run it yourself

If you'd rather keep everything on your own machine, there are two identical builds — pick whichever runtime you already have:

Node 18+ (no Python needed):

{
  "mcpServers": {
    "datasinking": {
      "command": "npx",
      "args": ["-y", "datasinking-mcp"],
      "env": { "DATASINK_API_KEY": "YOUR_KEY" }
    }
  }
}

Python 3.8+:

pip install "datasinking[mcp]"
datasinking-mcp          # requires DATASINK_API_KEY (free at https://datasink.ing)

Then use command: datasinking-mcp in your client.

Both run the same six tools with the same schemas — npm/ and datasinking/mcp_server.py are kept in lockstep by check_mcp_parity.py.

Full per-client setup: mcp-server.md (overview) · docs/mcp/ (one guide per client).

The hosted endpoint is POST-only and stateless — GET /mcp returns 405, and no session id is issued. Most clients read MCP config at launch, so restart one after changing its config.

What this repo is

Examples, research and tutorials showing how to work with financial report data, including reproducing the presentation styles found in financial-report research papers.

datasinking/
├── examples/     # Example scripts: pull data from the API and analyze it
├── research/     # Research notes / blog posts (reproducing paper-style presentation)
├── datasinking/  # Python client + MCP server — pip install "datasinking[mcp]"
├── npm/          # The same MCP server on npm — npx -y datasinking-mcp (Node 18+)
├── docs/mcp/     # Per-client MCP setup — Claude Code, Desktop, Cursor, Codex, WorkBuddy, Devin…
├── mcp-server.md # MCP server overview — endpoint, per-client table, tools, troubleshooting
├── llm-examples.md  # Ask an LLM — no code needed (8 end-to-end examples)
├── api-examples.md  # 7 examples × 3 interfaces (curl / Python / LLM)
└── README.md

Per-client MCP guides — one file each, with the exact config path, both scopes, verification and the errors that client actually produces:

Client	Guide
Claude Code	docs/mcp/claude-code.md
Claude Desktop	docs/mcp/claude-desktop.md
OpenAI Codex CLI	docs/mcp/codex.md
WorkBuddy / CodeBuddy	docs/mcp/workbuddy.md
Doubao Work / 豆包工作	docs/mcp/doubao.md
Cursor	docs/mcp/cursor.md
DeepSeek Harness (dsh)	docs/mcp/deepseek.md
Devin Desktop	docs/mcp/windsurf.md
OpenCode	docs/mcp/opencode.md
Qoder	docs/mcp/qoder.md
Reasonix	docs/mcp/reasonix.md

Quick start

1. Get an API key at datasink.ing
2. One line (FMP-style ?apikey=):

curl "https://api.datasink.ing/documents?symbol=600519.SS&with_content=1&apikey=YOUR_KEY"

Or in Python:

pip install datasinking

from datasinking import DataSinking

ds = DataSinking("YOUR_KEY")
for r in ds.get_stock_reports("600519.SS", limit=3):
    print(r["report_period"], r["title"], len(r["content"]), "chars")

All five functions (curl / Python / LLM): api-examples.md.

Ask an LLM (no code)

Don't want to write code? Point any LLM at datasink.ing, give it your API key, and ask in plain language. See llm-examples.md for eight end-to-end examples — explore coverage, list a company's reports, and extract a figure with correct units.

Examples (examples/)

File	What it does
01_quickstart.py	The 5 core functions: list exchanges / stocks / reports / fetch a report / fetch a stock's reports
02_download_company.py	Download a company's full reports to local Markdown files
03_download_exchange.py	Download an entire exchange's reports (all stocks) to local Markdown files
rag_ingest.py	Slice reports into RAG chunks (on the `` anchors, with source traceability) and ingest into a vector store

Every example pulls from the live API and runs as-is.

03_download_exchange.py fetches every report on an exchange (e.g. all of Shenzhen — 150k+ documents). Quotas count documents, not requests, over a rolling 7-day window: a free key gets 3 req/s and 8,191 documents per 7 days, inside a pool of 524,287 per 7 days shared by all free users and website visitors. A whole exchange will therefore take well over a week on a free key — a paid (yearly) key (31 req/s, 524,287 documents per 7 days) is strongly recommended.

Research (research/)

research/ hosts research notes and blog posts, each based on DataSinking data with the source cited. You can reproduce charts and presentations found in financial-report research papers, e.g.:

• Long-term revenue / profit trends
• Industry comparison and distribution
• Time series of financial metrics

Start from research/TEMPLATE.md.

Data overview

Coverage	US (NYSE / NASDAQ / CBOE) · China (SSE / SZSE / BSE) · Japan (TSE) · Korea (KOSPI / KOSDAQ / KONEX) · Taiwan (TWSE / TPEx)
Document types	annual / semiannual / q1 / q3 / amendment
Update frequency	Daily — US/Korea/Japan via official SEC EDGAR / DART / EDINET APIs (new filings within ~24h of publication)
Format	Full-text Markdown (with YAML frontmatter)
API	REST — GET /documents, batch download, with_content=1 for full text, ?section= + /sections for chapter-level access
Symbols	FMP style: AAPL / 600519.SS / 7203.T / 005930.KS
Auth	?apikey= query parameter (FMP style)

Data source

Reports are sourced from the official regulatory disclosure platform of each market and converted in-house to clean Markdown:

Market	Source	Platform
US (bare ticker, e.g. AAPL)	SEC EDGAR	US Securities and Exchange Commission — sec.gov/edgar
China A-shares (.SS .SZ .BJ)	巨潮资讯网 cninfo	CSRC-designated disclosure platform
Korea (.KS .KQ .KN)	DART	Financial Supervisory Service — opendart.fss.or.kr
Japan (.T)	EDINET	Financial Services Agency — disclosure2.edinet-fsa.go.jp
Taiwan (.TW .TWO)	公開資訊觀測站 MOPS	Taiwan Stock Exchange — mops.twse.com.tw

Every document also carries a source field in the API response, so the attribution travels with the data. Please keep it when you redistribute.

License

MIT