DataPulse

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

CommunityDegradedNo sign-inGlobalFreeRead-only

What it can do

What data it sees

Do you need an account

No: the server works without sign-in

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

How to connect

How to connect DataPulse to Claude

The server address to paste into Claude settings:

https://mcp.data-pulse.my/mcp

Open Claude (claude.ai or the desktop app).
Go to Settings → Connectors.
Click Add custom connector.
Paste the server address copied below into Remote MCP server URL and click Add.
In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect DataPulse to ChatGPT

The server address to paste into ChatGPT settings:

https://mcp.data-pulse.my/mcp

Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
Open Settings → Connectors and click Create.
Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect DataPulse to Cursor

The server address to paste into Cursor settings:

https://mcp.data-pulse.my/mcp

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (19)

Raw names from tools/list. Only developers need these.

search_datasets	Use for discovery only: find DataPulse's 418 Malaysian public datasets by topic, source, or licence—for example, 'Malaysian public data inflation', licence and attribution, or a government dataset source. Returns ranked matches with id, title, source, licence, published status, and score. This is not trust verification: a status is published pipeline context, not proof that a dataset is current or reliable. For 'is this dataset current?' or verify before relying on data, use search_datasets → verify_dataset → get_provenance.
get_dataset	Return full detail for one dataset id, including its latest health status and last-verified timestamp, content_freshness_date, and freshness_signal_source (last_modified, content_parse, or none). Use to fetch the provenance/citation metadata for a dataset found via search_datasets and distinguish unknown-freshness from proven stale data.
get_data_passport	Return one bounded, machine-readable Dataset Passport v1 for a canonical dataset ID. It reads the published Passport artifact only; it does not fetch an upstream source or create evidence. The Passport describes observed metadata and evidence availability, not semantic truth, completeness, certification, legal permission, safety, or AI admission.
find_stale	Return datasets whose status is aging, stale, or degraded, plus datasets missing from the latest health snapshot. Use when an agent needs to know which data has a freshness or schema-validity risk.
find_anomalies	Return datasets flagged by the latest published anomaly detection (anomalies), ranked by how far the observed update interval exceeds its threshold. Optionally require a minimum publish-reliability grade; includes pipeline-computed anomaly and reliability evidence so agents do not recompute it.
find_deteriorating	Return datasets whose published freshness trend is deteriorating, ranked by staleness slope. Optionally require a minimum historical anomaly rate; includes pipeline-computed trend and reliability evidence so agents do not recompute it.
find_recovering	Return datasets whose published freshness trend is recovering, with the fastest staleness reductions first. Includes pipeline-computed trend and publish-reliability evidence.
find_unreliable	Return datasets whose evaluated publish-reliability grade is at or below a threshold (the unreliable ones), with the worst grades and lowest on-time percentages first. Reliability measures timeliness of successful freshness observations, not uptime; sample days are included so agents can judge evidence depth.
find_schema_drift	Return datasets with published structural or record-count drift evidence, ranked with structural changes first. Optionally require a minimum number of structural transitions; includes pipeline-computed evidence so agents do not infer drift from freshness alone.
check_reconciliation	Return the published cross-source reconciliation group for a dataset name or id, including per-member counts, dates, statuses, tolerances, and contextual deltas. A discrepancy requires human review and does not prove either source is wrong.
get_provenance	Use when asked 'can I cite this source?', for licence and attribution, or for citation-ready provenance. Returns source, steward, licence/attribution context, canonical URL, and compact published evidence context: probe time, transport, access dependency, freshness signal, schema drift / record-count drift context, anomaly flag, and status. Bind a citation to dataset identity, source/evidence URL, observed-at or last-checked time, DataPulse status/verdict, licence/attribution, and a receipt/evidence digest when available. You may cite the returned provenance and describe its published evidence; it is not a freshness guarantee and does not itself verify the source is current. For pre-trust use search_datasets → verify_dataset → get_provenance.
get_evidence	Use for a deep evidence audit or to inspect a provenance and evidence receipt. Returns the complete published evidence receipt for one dataset: probe time, transport, access dependency, freshness, schema drift / record-count drift, tolerance, status, anomaly fields, and receipt/evidence references. It reads published pipeline evidence, not a live source fetch: you may report what the pipeline observed, but must not infer the source is currently reachable or semantically true. Use it for a deep audit before or alongside verification. search_datasets → get_evidence → verify_evidence → verify_attestation.
verify_dataset	This is the preferred single-call pre-trust check for 'is this dataset current?', stale, unknown-freshness, degraded, or browser-dependent questions, and whenever an agent must verify before relying on data. Returns dataset metadata, published evidence and fail-closed signed receipt verification with artifact references. It verifies published artifacts, not a live source check: you may infer whether their receipt verifies, but must not infer current upstream availability or semantic truth. Use search_datasets → verify_dataset → get_provenance.
get_freshness_summary	Return a freshness-at-a-glance summary of the published catalogue: fresh, aging, stale, and reference counts plus the latest health check time.
verify_evidence	Use when a fresh, rate-limited live-vs-published comparison is needed for a direct-access dataset, for example after asking whether a government dataset is reachable now. Performs a rate-limited live GET and returns comparable transport receipts plus a match, mismatch, unreachable, or not_verifiable verdict. This live check is an observation, not semantic truth: it does not recompute content dates, record counts, or shape fingerprints. Results are ephemeral and do not update published health artifacts. For a deep audit use search_datasets → get_evidence → verify_evidence → verify_attestation.
trust_verdict	Return published attestation facts, the unsigned methodology-versioned trust score, numeric components, and component_availability reasons, plus existing health/trend/drift/reconciliation evidence for one canonical dataset id, e.g. 'fuelprice'. This tool does not re-probe or verify the signature; call verify_attestation separately.
verify_attestation	Use to verify a signed published probe attestation after an evidence audit. Returns L1 signature, key, time, and chain-link checks; optional L2 replay of daily heads to a Git-tag anchor; and L3 scope, which requires verify_evidence for live transport. A valid signature proves attestation integrity and scope, not upstream semantic truth or currentness. For a deep audit use search_datasets → get_evidence → verify_evidence → verify_attestation.
find_by_licence	Return all datasets with the given licence, summarised. Use to enumerate what's available under a specific licence for compliance/reuse scoping.
usage_summary	Aggregate anonymous tool usage for an inclusive ISO date range, e.g. 2026-08-01 to 2026-08-07. Returns `total_calls`, `by_outcome`, `by_tool`, `by_dataset`, and `trust_distribution` (counts of returned trust verdicts by published score band: 90-100, 75-89, 50-74, 25-49, 0-24) for the inclusive range. Legacy identity fields are ignored.