What is Chinese A-Share Stock Data?

39/100
Trust Score (E)
🔴 Avoid

Chinese A-Share Stock Data is a AI tool that Provides tiered stock analysis for Chinese A-share markets with real-time quotes, technical indicators, and financial data via AkShare.. It has a Nerq Trust Score of 39/100 (E). 0 GitHub stars. Published by https://github.com/huang1125677925/stock_mcp. Last analyzed September 2026.

Why This Score

Trust & Safety Overview

39
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What Chinese A-Share Stock Data Does

Chinese A-Share Stock Data is a mcp_server in the AI tool category. Provides tiered stock analysis for Chinese A-share markets with real-time quotes, technical indicators, and financial data via AkShare.. It is published by https://github.com/huang1125677925/stock_mcp and has no specified license. With 0 GitHub stars and 0 downloads, it has a small community of users and contributors.

Who Should Use Chinese A-Share Stock Data

Chinese A-Share Stock Data is recommended only for experimental use. Consider alternatives with higher trust scores for production systems.

Details

Authorhttps://github.com/huang1125677925/stock_mcp
CategoryAI tool
LicenseNot specified
Typemcp_server
SourceView on GitHub
Security Score0/100
Activity Score0/100

How to Get Started

Check the trust score before installing:

curl nerq.ai/v1/preflight?target=chinese-a-share-stock-data

Setup guide · Full safety report · Production review · Is it safe?

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Frequently Asked Questions

What is Chinese A-Share Stock Data used for?
Chinese A-Share Stock Data is a AI tool tool. Provides tiered stock analysis for Chinese A-share markets with real-time quotes, technical indicators, and financial data via AkShare..
Is Chinese A-Share Stock Data free?
License: Check project page. Chinese A-Share Stock Data has 0 GitHub stars.
Is Chinese A-Share Stock Data safe?
Chinese A-Share Stock Data has a Nerq Trust Score of 39/100 (E). Evaluate carefully.
What are alternatives to Chinese A-Share Stock Data?
Top alternatives: MarkItDown, Fetch, Time. See full comparison.

Last updated September 2026. Trust scores based on automated analysis of public data.

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