by hike-lab · MCP Server · ★ 81
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MCP-based discovery and judgment layer for Korea’s open data portal—helping AI agents find, compare, and assess datasets with explicit evidence, JSON-LD/DCAT metadata, and versioned deterministic rules.
| Stars | 81 |
| Forks | 19 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 54.6029118118812/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-25 |
| Created | 2026-08-03 |
| Platforms | mcp, python |
| Est. Tokens | ~13k |
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국토교통부 건축HUB → MCP 서버. 건축물대장·건축인허가·주택인허가 + 법정동코드 + 한 필지 종합카드·동 단위 통계·노후건축물 분석. 공용키 탑재 remote 커넥터(URL만 등록)
다이소를 MCP로! 뿐만 아니라 한국 로컬 리테일과 영화관 조회를 MCP, CLI, Codex Skill로 가능하게 해줍니다.
Google Analytics 4 data to AI agents, agentic workflows, and MCP clients. Give agents analysis-ready access to
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public-data-lens is MCP-based discovery and judgment layer for Korea’s open data portal—helping AI agents find, compare, and assess datasets with explicit evidence, JSON-LD/DCAT metadata, and versioned deterministic rule. It is categorized as a MCP Server with 81 GitHub stars.
public-data-lens is primarily written in Python. It covers topics such as ai-agents, ai-ready-data, data-catalog.
You can find installation instructions and usage details in the public-data-lens GitHub repository at github.com/hike-lab/public-data-lens. The project has 81 stars and 19 forks, indicating an active community.
public-data-lens is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to public-data-lens on Agent Skills Hub include FirstData, ckan-mcp-server, ontosphere. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: