No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by yimindev · MCP Server · ★ 58
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is dati safe to install? View the security audit →
DatI - Database Semantic Gateway for AI Agents English | 简体中文 DatI (Data Intelligence) is a lightweight semantic gateway connecting AI Agents with enterprise databases. Simply connect your database, enrich semantic metadata, and configure built-in or parameterized SQL tools to publish an MCP service — ready for your agents or any MCP host. Try It Online Demo URL: http://47.99.122.223:18085/ Account / Password: /
| Stars | 58 |
| Forks | 4 |
| Language | Java |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 58.2249138755555/100 |
| Last Updated | 2026-09-29 |
| Created | 2026-08-23 |
| Platforms | cli, java, mcp |
| Est. Tokens | ~17k |
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dati is Turn your database into secure, semantically rich MCP tools for agents.. It is categorized as a MCP Server with 58 GitHub stars.
dati is primarily written in Java. It covers topics such as access-control, agentic-ai, business-intelligence.
You can find installation instructions and usage details in the dati GitHub repository at github.com/yimindev/dati. The project has 58 stars and 4 forks, indicating an active community.
dati is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to dati on Agent Skills Hub include orionbelt-semantic-layer, orionbelt-semantic-layer, MCP-PostgreSQL-Ops. 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: