yimindev/dati
by Various
Turn databases into secure, semantically-rich MCP tools for AI Agents. Built-in NL2SQL, schema metadata inspection, and parameter query templates.
MCP
yimindev/dati
Added 16 Sept 2026
Overview
Converts databases into MCP tools for AI agents. Includes natural language to SQL, schema metadata inspection, and parameterized query templates. Written in Java.
Best for
Best for
Java developers who want to give AI agents secure, structured access to databases via MCP.
Use cases
- Expose database tables as MCP tools for AI agents
- Let agents query data using natural language
- Inspect schema metadata before generating queries
Notes
Converts databases into MCP tools for AI agents. Includes natural language to SQL, schema metadata inspection, and parameterized query templates. Written in Java.
23 stars on GitHub. Last updated 2026-09-13. Licensed Apache-2.0.
Use cases
- Expose database tables as MCP tools for AI agents
- Let agents query data using natural language
- Inspect schema metadata before generating queries
Pros
- Built-in NL2SQL simplifies natural language access
- Schema metadata inspection helps agents understand data structures
- Parameter query templates support controlled, repeatable queries
Cons
- Very early-stage project with only 23 stars
- Java-only, which limits adoption outside the JVM ecosystem
- Security depends on careful configuration of query templates
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Built-in NL2SQL simplifies natural language access
- Schema metadata inspection helps agents understand data structures
- Parameter query templates support controlled, repeatable queries
Cons
- Very early-stage project with only 23 stars
- Java-only, which limits adoption outside the JVM ecosystem
- Security depends on careful configuration of query templates
Open-source & AI alternatives
Swap-in tools that solve the same job. Weigh the trade-offs before you commit.
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