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r3dz4r/datapulse-my

r3dz4r/datapulse-my

Trust checks for Malaysian public datase
在 GitHub 查看 ↗直链 · 无中转页
配置模板(请按你的客户端调整)
claude mcp add datapulse-my -- npx -y datapulse-my
这是根据仓库名生成的可粘贴骨架,命令行参数与包名请以该项目 README 为准。
★ 数取值
2 ★ · r3dz4r/datapulse-my · 取自 GitHub 搜索 API · (2026-10-16 20:00:20 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
评分构成
综合分 703 · 2 ★ · 7 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 r3dz4r/datapulse-my;我们收录于 2026-10-09;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

DataPulse

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

**Live dashboard:** https://www.data-pulse.my
**Open in Google Colab:**
-success?style=flat&logo=anthropic)
> **🤖 AI-agent-ready** — Wire DataPulse into Claude Desktop, Cursor, Cline, or
> any MCP-compatible client with one config block. Your agent gets
>
**440 official Malaysian datasets** — including **30 GTFS transit feeds (KTMB,
Prasarana, BAS.MY)** — with declared licences and an honest ten-status trust
taxonomy instead of a blanket green checkmark.
>
> → Connect your AI agent in 30 seconds
This is DataPulse
When an AI quote is wrong, it is often wrong because the **underlying data was
stale, mis-licensed, or unverifiable** — not because the model hallucinated.
An official-looking page does not tell an agent when the dataset behind it last
updated, who published it, whether it may legally be reused, or whether the
observation can be reproduced by a second party.
DataPulse is an open, read-only verification layer for Malaysian public data. It continuously observes Malaysia's official open datasets — those published through data.gov.my, Bank Negara Malaysia, DOSM, the Department of Environment, the Ministry of Health, KPDN and MET Malaysia — and publishes machine-readable evidence about each one: whether the source is reachable, how fresh its content is, which licence applies, whether its structure or record count has changed, and when the observation was signed. Every dataset carries one of ten explicit statuses instead of a blanket green tick, and each published observation can be checked by a third party: receipts are signed, the signing keys are published with validity windows and rotation history, and a single-file verifier reproduces the check with no DataPulse installation. DataPulse does not replace the official source and does not certify that a publisher's data is substantively correct — it makes the condition of the source observable, and the observation reproducible.
**440 official datasets**
What we do, simply
- **We watch the sources.** A scheduled probe revisits each dataset under its
declared cadence and records what it actually finds — reachability, an honest
freshness signal, schema shape, record counts, and collection quirks.
- **We state the truth plainly.** Instead of a blanket green checkmark, each
dataset carries one of ten honest health statuses (fresh, aging, stale,
discontinued, degraded, browser-dependent, unreachable, unknown,
unknown-freshness, reference). A dataset that cannot be proven fresh is
labelled unknown-freshness — not silently treated as healthy.
- **We publish evidence, not just claims.** Each dated observation is signed
and recorded to an immutable public log, so you can verify *when* DataPulse
observed the source and that the record has not been altered.
- **We make it machine-readable first.** The whole portfolio is discoverable
from one index and queryable over a read-only MCP server, so an agent receives
the same freshness, licence, and provenance signal a careful human reviewer
would.
Who this serves
- **AI builders and agent developers**, who want a model to check a Malaysian
figure's freshness and licence before it cites the number — without building a
bespoke integration or trusting a scraping pipeline.
- **Researchers, analysts, and journalists**, who need to ground coursework,
a thesis, a dashboard, or a published figure in data whose currency and licence
they can actually verify.
- **Compliance and regulatory-monitoring teams**, who must keep a tamper-evident
trail that an official figure was checked at a known time before it reached a
product or a public statement.
- **Civic technologists and public servants**, who want a transparent,
reproducible view of how discoverable and reliably described public data is.
Why you can trust the verification
Three independent, checkable layers. You do not have to take DataPulse's word —
you can verify each with the published public key, the public Git source record,
and the public transparency log:
| Layer | What it proves | How to check it yourself |
|---|---|---|
| **Signed envelope** | Each per-dataset observation is **Ed25519-signed** over its exact content by a key in the published registry | python3 scripts/verify_external.py |
来源
awesome · https://github.com/r3dz4r/datapulse-my
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