MCP is the open protocol for AI agents — Claude, OpenClaw, Hermes, your own agent loop. Twinkle Hub bundles Taiwan data sources behind a single MCP endpoint — set your client up once, AI can query datasets, run SQL, and call tools.
✓Real-estate (LVR) + procurement (PCC) now carry district codes — one town_code JOIN across datasets
Try asking:
Average 2024 home price in Xinyi District, Taipei
Which district is coordinate (121.57, 25.03) in
2026-05-16v1.14.1
Kaohsiung Community Card — first community partner
datasets
2
✓119 Kaohsiung tech-community events for 2026 (GDG / TOOCON / PyLadies / KIMU / Build with AI…)
✓13 communities + stamp-card rewards + sponsor list
✓Zero install: no npm, no GitHub token — just ask in Claude Desktop
Try asking:
Tech events in Kaohsiung this May
What is GDG Kaohsiung running this month
Why this exists
Taiwan data is scattered across 100+ portals. AI app builders don't have time to wire each one.
Twinkle Hub bundles them into a single MCP service: discover datasets, fetch rows, join across tables, pay — all on one endpoint. Phase one is making data.gov.tw excellent; more sources (industry associations, local governments, commercial data) come next.
01
One MCP endpoint
Every source, every domain, every tool under the same URL. Configure your client once.
02
Deterministic billing
Fixed price per tool — no token-based gambling. Prepaid wallet, hard cutoff, no surprise bills.
03
License compliance
Original license metadata is passed through end-to-end so downstream apps don't trip license terms.
04
MCP-native protocol
tools/list / tools/call are native endpoints, not OpenAI tool-calling translated. Claude / OpenClaw / Hermes / Continue / Cline / your own agent loop — same wire format, no SDK, no adapter.
Why Twinkle Hub
Downloading 53,000 datasets is easy. Making them usable to AI is the hard part.
Making Taiwan open data usable to AI isn't a one-off catalog download. We handle the cleanup, conversion, classification, and querying — your AI just calls the API instead of crawling a hundred different portals.
52,960 / 19
53,000 datasets, sorted automatically
All 52,960 datasets on data.gov.tw, sorted into 19 categories. New ones land in the right place the next day — the index never ages out.
10+ formats
All the file formats, one shape
CSV, JSON, Excel, PDF, geo files — whatever the format, we read it and clean up the column names. Your AI doesn't choke just because one source is PDF and another is XLSX.
~50 ms
Ask a question, answer in 50 ms
Want your AI to query data on specific conditions? We have a query layer — about 50 ms across all 53,000 datasets. We don't dump a zip and tell you to process it yourself.
Daily refresh
Synced with the government every day
A daily cron pulls the latest catalog, picks up what's new or retired, runs classification. The data stays fresh — it doesn't go stale because we forgot to update.
Beyond the data
One thing we wired up alongside
OpenData is the main course. This one also matters for agents — we did it too.
20 official Anthropic Agent Skills. Load one into Claude Desktop / Claude Code / GitHub Copilot CLI / OpenAI Codex CLI — or any MCP-compatible agent — and it already knows which tool to call, which filter to use, and what a typical query looks like.
2026-05-31 internal benchmark — Phase 1 plan-only across 11 skill domains, Claude Opus 4.7 subagent A/B (incl. Round 2 narrow-domain: health/finance/environment/agriculture); Phase 2 real MCP (LVR query: "Taipei Da'an 2024 transactions over NT$50M"). Single-query sample; cold-start and retry costs not included. See full report for caveats.
