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Datapulse My MCP Server

by R3dz4r
Developer ToolsLow Risk10.0MCP RegistryRemote
Free

Server data from the Official MCP Registry

Read-only discovery for 418 Malaysian public datasets with freshness, licence, and provenance.

About

Read-only discovery for 418 Malaysian public datasets with freshness, licence, and provenance.

Remote endpoints: streamable-http: https://mcp.data-pulse.my/mcp

Security Report

10.0
Low Risk10.0Low Risk

Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. Trust signals: trusted author (3/3 approved).

Endpoint verified · Open access · No issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

HTTP Network Access

Connects to external APIs or services over the internet.

How to Connect

Remote Plugin

No local installation needed. Your AI client connects to the remote endpoint directly.

Add this to your MCP configuration to connect:

{
  "mcpServers": {
    "io-github-r3dz4r-datapulse-my": {
      "url": "https://mcp.data-pulse.my/mcp"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

DataPulse

Live dashboard: https://www.data-pulse.my

Open in Google Colab: Open In Colab

datapulse-my MCP server M8ven Verified mcpgrade

🤖 AI-agent-ready — Wire DataPulse into Claude Desktop, Cursor, Cline, or any MCP-compatible client with one config block. Your agent gets

418 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 exists to make that uncertainty explicit. It is an open, read-only verification layer for Malaysian public data: it continuously probes 418 official datasets, and publishes — for each one — machine-readable evidence about whether the source is reachable, how fresh the content is, what licence applies, how the schema behaves, and when the observation was signed.

It does not replace the official source. It documents, on an honest and reproducible basis, what the official portal states and whether that material is current, so you know what you are reusing or citing. The verification speaks for itself: every claim here is a live, checkable artefact, not a promise.

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:

LayerWhat it provesHow to check it yourself
Signed envelopeEach per-dataset observation is Ed25519-signed over its exact content by a key in the published registrypython3 scripts/verify_external.py
Source of recordThe served observation byte-matches the versioned Git sourcepython3 scripts/verify_external.py
Temporal witnessThe health statement carries a Rekor/Sigstore public-log inclusion proofpython3 scripts/verify_external.py

Run it yourself, from anywhere, with no checkout and no DataPulse code:

curl -fsSLO https://raw.githubusercontent.com/r3dz4r/datapulse-my/main/scripts/verify_external.py
python3 verify_external.py

See Verify DataPulse externally for the full guide, and our methodology below for how health is judged.

A verification layer is only as honest as its method, so DataPulse deliberately tells you when it cannot be sure — a source that cannot be proven current is labelled accordingly, never silently marked healthy. That is the boundary we hold: the platform proves the integrity and timing of its observations, not that an upstream government figure is semantically true. That distinction is the whole point of an evidence layer, and we do not blur it.

Dataset health

Health is reported as fresh, aging, stale, discontinued, degraded, browser-dependent, unreachable, unknown, unknown-freshness, or reference. Unknown freshness means the URL and content shape work, but neither a Last-Modified header nor a parseable content date proves when the data was updated. Reference means versioned lookup data is reachable and its record count is measured, while date-based freshness does not apply. Within the catalogue, data_type refines the reference family without changing the status: policy-reference rows (policy state that stays valid until superseded — BNM OPR is current while unchanged, not stale) and reference-current rows (lookups that must still pass freshness, such as a bank-rate table that can itself go stale) are judged by their declared policy, while plain reference rows are static. The public _trust_summary shows the distribution and explicitly counts missing freshness and row-count signals.

Discontinued — The source has stopped publishing new data. The data is frozen at the last known content date. This is not a freshness failure — it's a publisher decision.

Current distribution (_trust_summary): 90 fresh · 113 aging · 187 stale · 1 discontinued · 1 degraded · 5 browser-dependent · 7 unknown-freshness · 14 reference

Subscribe: RSS feed — get notified when dataset health changes.

Browser-dependent datasets

Five sources (currently 1.4% of the catalogue) require a real browser to probe because their source pages render client-side JavaScript: eperolehan-diklankan, doe_apims, doe_rqims, doe_mqims, and kkm_idengue.

DataPulse uses Camofox, a self-hosted patched headless-Chromium sidecar, to probe these. The probe path is check.sh → Camofox sidecar → DOM snapshot → content-date extraction.

To enable browser probing:

  1. Run the Camofox Docker sidecar on a reachable address (default http://localhost:9377). The probe script and the GitHub Actions workflow pick this up from the CAMOFOX_BASE_URL environment variable; nothing in this repo encodes a public IP.
  2. Set CAMOFOX_BASE_URL to that address.
  3. Restart the timer with systemctl restart datapulse-health.timer.

Without Camofox, those five datasets will sit at browser-dependent — the honest status: DataPulse cannot probe them without a browser, so it says so rather than failing silently. See scripts/smoke_browser_probes.sh for isolated smoke tests.

Methodology

TopicDataPulse's position
Health statusTen-status taxonomy, judged by reachability + an honest freshness signal (Last-Modified, parseable content date, or declared policy) — never a fabricated green checkmark. A series that stopped publishing is discontinued (a publisher decision, frozen data), not a freshness failure.
LicenceEvery dataset declares its licence machine-readably. Creative Commons Attribution 4.0 (285); MBPP Government Open Data Terms (attribution required) (1); MIT License (8); Open Government Licence (Malaysia) (115); Publisher licence not stated; portal disclaimer applies (4); Singapore Open Data Licence v1.0 (attribution required) (5). A second party can reproduce this from datapulse.json → .datasets[].licence.
Freshness cadenceEach dataset is probed on its own tiered schedule (5-minute timer, cadence-aware) — daily references, weekly fuel prices, monthly surveys, etc. Always with the human-readable steward and a stable custodian ID for publisher provenance.
ProvenanceStable custodian per dataset; signed probe attestations per observation
Observed claimThe platform proves what an official source was observed to be at a known time — it does not claim upstream data is semantically true
Read-only + lawfulPublicly available, authenticated sources only — never bypassed; rate-limited; identifies itself to sources
VerificationFresh days are Rekor-witnessed; signed envelopes + Git source-of-record + public-log inclusion, checkable by anyone

Connect an AI agent

DataPulse exposes an AI-ready, read-only MCP server so agents can query the catalogue natively. It provides the same freshness, licence, schema-drift, and provenance evidence available to a human reviewer.

  • Endpoint: https://mcp.data-pulse.my/mcp (Streamable HTTP, no auth) Graded by mcpgrade — replay with bash scripts/audit_mcpgrade.sh (pinned version, writes artifacts/mcpgrade/). The canonical tool count lives in mcp.json / agent.json.

  • 19 tools: search_datasets, get_dataset, get_data_passport, find_stale, find_anomalies, find_deteriorating, find_recovering, find_unreliable, find_schema_drift, check_reconciliation, get_provenance, get_evidence, verify_dataset, get_freshness_summary, verify_evidence, trust_verdict, verify_attestation, find_by_licence, usage_summary

The public endpoint serves all 19 read-only tools over the 418-dataset catalogue.

get_evidence exposes pipeline receipts; verify_evidence performs cached transport-only live checks and does not update health.

Connect from Claude Desktop:

{
  "mcpServers": {
    "datapulse-my": {
      "transport": "streamable-http",
      "url": "https://mcp.data-pulse.my/mcp"
    }
  }
}

See llms.txt for the full discovery index, and docs/mcp-deploy.md for the deployment architecture.

Wire it into Claude Desktop via claude_desktop_config.json (30 seconds, no API key):

{
  "mcpServers": {
    "datapulse-my": {
      "transport": "streamable-http",
      "url": "https://mcp.data-pulse.my/mcp"
    }
  }
}

Restart Claude Desktop, confirm the hammer icon shows "datapulse-my" with the read-only tools listed above. Cursor / Cline use the same JSON in their MCP config panel.

Included datasets

Dataset inventory is grouped by stable custodian publisher ID; unknown IDs fall back to the ID itself.

National Anti-Drugs Agency (aadk)

Accountant General's Department of Malaysia (agc)

Agensi Pengangkutan Awam Darat (APAD) (apad)

Bank Negara Malaysia (bnm)

Department of Agriculture Malaysia (doa)

Department of Environment Malaysia (doe)

Department of Fisheries Malaysia (dof)

Department of Statistics Malaysia (dosm)

Energy Commission (energy_commission)

Documentation truncated — see the full README on GitHub.

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