Mmcp.market

DataSinking MCP server

by heubme2020·io.github.heubme2020/datasinking·v0.2.2

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

A92/100grade A
What users say
No reviews yet
Be the first
Safety scan
A92/100

full report

Adoption
Growing

16 stars474 downloads/wk

Reviews

Write one

Nobody has reviewed DataSinking yet.

If you have run it, two minutes of your experience saves the next person an afternoon.

DataSinking tools (6)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • get_report

    Fetch a single report's full text (metadata + Markdown body).

  • get_section

    Fetch only one section of a report by keyword — cheaper than get_report for RAG.

  • list_exchanges

    List the exchanges DataSinking covers and their report counts.

  • list_reports

    List a company's reports — metadata only (id, title, period), no body text.

  • list_sections

    List every section heading in a report (feed the headings to get_section).

  • list_stocks

    List stocks on an exchange, including the report count per company.

Public scan report

scanner v0.1.9 · 2026-09-20 · same rubric, same numbers if you re-run it

no findings
  • Code scan4 source files scanned25/25
  • Live reliabilityno gateway calls yet and no remote to proben/a
  • Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 6 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 92/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

What the publisher says

From the DataSinking repository's README, as published. We do not edit it. Read it on GitHub

DataSinking

<!-- mcp-name: io.github.heubme2020/datasinking -->

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

DataSinking serves full-text financial reports — annual, semi-annual and quarterly — 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 — 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 — 6 tools: list exchanges, list stocks, list reports, fetch a report, list sections, fetch one section (token-friendly for RAG).

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.

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]"
├── mcp-server.md # How to configure the MCP server (for AI agents: Claude / Cursor / Codex / DeepSeek)
├── 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

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/)

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

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Install directly

claude mcp add datasinking -- uvx datasinking
Add to Cursor

DataSinking: common questions

Is DataSinking MCP server safe?
Yes, by our scan: it is graded A (92/100). Read the DataSinking safety report
How do I install DataSinking?
It runs on your machine. Copy the Claude Code, Claude Desktop or Cursor config from the install section.
Does DataSinking need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is DataSinking maintained?
The last commit was 7 days ago (2026-09-14). The latest release is v0.2.2.
What can I use instead of DataSinking?
Servers from other publishers that do the same job: AgentMD MCP server, IWE MCP server and Local Rag MCP server. Compare all DataSinking alternatives.

Alternatives to DataSinking

Same job from other publishers: the closest match first, then the best rated.

All DataSinking alternatives →
  • AgentMD
    Convert PDF, DOCX, HTML, and URLs to clean, LLM-ready markdown with tables preserved
    A
  • IWE
    Markdown knowledge base as agent memory. Runs against the notes directory it is started in.
    A
  • Local Rag
    Easy-to-setup local RAG server with minimal configuration
    A
  • Google Surf
    Web, academic and code search with graph RAG, data lineage, ontology and cross-project schema links.
    B
  • PDF
    Agentic RAG over one PDF or a whole folder: hybrid search, selective page reads, tables, OCR.
    A

More from heubme2020