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DataSinking

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Full-text Asian financial reports (China, Korea, Japan) as clean Markdown β€” 6 tools for RAG agents.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "datasinking": {
      "command": "npx",
      "args": [
        "-y",
        "datasinking"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ’° More in Finance & Fintech

Documentation Overview

DataSinking

PyPI version MCP server

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

DataSinking serves full-text financial reports β€” annual, semi-annual and quarterly β€” from China, Korea and Japan as clean Markdown, ready for LLM reading and RAG. Query by FMP-style symbol (600519.SS, 005930.KS, 7203.T) or filter by exchange, report period, or section β€” pull just the MD&A / risk section instead of the whole report. Reports are sourced from official disclosure platforms and parsed into structured Markdown with YAML frontmatter, preserved headings, paragraphs and tables.


MCP server

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

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

Or add to your client with command: datasinking-mcp. A remote streamable-HTTP endpoint is also live at https://api.datasink.ing/mcp. See mcp-server.md.

DataSinking MCP in Claude

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.

Code
datasinking/
β”œβ”€β”€ examples/     # Example scripts: pull data from the API and analyze it
β”œβ”€β”€ research/     # Research notes / blog posts (reproducing paper-style presentation)
β”œβ”€β”€ datasinking/  # Python client + MCP server β€” pip install "datasinking[mcp]"
β”œβ”€β”€ mcp-server.md # How to configure the MCP server (for AI agents: Claude / Cursor / Codex / DeepSeek)
β”œβ”€β”€ llm-examples.md  # Ask an LLM β€” no code needed (8 end-to-end examples)
β”œβ”€β”€ api-examples.md  # 7 examples Γ— 3 interfaces (curl / Python / LLM)
└── README.md

Quick start

  1. Get an API key at datasink.ing
  2. One line (FMP-style ?apikey=):
Terminal
curl "https://api.datasink.ing/documents?symbol=600519.SS&with_content=1&apikey=YOUR_KEY"

Or in Python:

Terminal
pip install datasinking
server.ts
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 β€” explore coverage, list a company's reports, and extract a figure with correct units.

Examples (examples/)

FileWhat it does
01_quickstart.pyThe 5 core functions: list exchanges / stocks / reports / fetch a report / fetch a stock's reports
02_download_company.pyDownload a company's full reports to local Markdown files
03_download_exchange.pyDownload an entire exchange's reports (all stocks) to local Markdown files

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 β€” 150k+ documents). Free keys work too, but fall back to slow per-document fetching (1 req/s + shared daily quota); a paid (yearly) key is strongly recommended for full-exchange downloads.

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

CoverageChina (SSE / SZSE / BSE) Β· Korea (KOSPI / KOSDAQ / KONEX) Β· Japan (TSE)
Document typesannual / semiannual / q1 / q3 / amendment
Update frequencyDaily β€” Korea/Japan via official DART/EDINET APIs (new filings within ~24h of publication)
FormatFull-text Markdown (with YAML frontmatter)
APIREST β€” GET /documents, batch download, with_content=1 for full text, ?section= + /sections for chapter-level access
SymbolsFMP style: 600519.SS / 005930.KS / 7203.T
Auth?apikey= query parameter (FMP style)

Data source

Reports are sourced from official regulatory disclosure platforms in each market and converted in-house to clean Markdown.

License

MIT

Read the full README β†’View source on GitHub β†’

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Reviews

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Frequently Asked Questions about DataSinking

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "datasinking": { "command": "npx", "args": ["-y", "DataSinking"] } }

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Technical Specs & Signals

CategoryπŸ’°Finance & Fintech
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
Views0
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
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Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
28Quality signal: Emerging Β· 28/100How this signal is calculated β–Ύ
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Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools12/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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