XiYan MCP Server: Finally, Reliable Natural Language Database Queries

Stop wrestling with ChatGPT's inconsistent SQL generation. XiYan MCP Server brings state-of-the-art text-to-SQL directly into your development workflow, powered by purpose-built models that actually understand your database schema.

The Database Query Problem You Know Too Well

You've tried getting LLMs to write SQL. Sometimes it works brilliantly, other times it hallucinates table names or completely misunderstands your intent. Generic models weren't built for the precision that database queries demand—they're great at conversation, terrible at generating reliable, executable SQL.

XiYan solves this with XiYanSQL-QwenCoder-32B, a specialized model that leads text-to-SQL benchmarks. Instead of hoping GPT-4 remembers your schema correctly, you get a model trained specifically for database interactions.

What Makes This Different

Purpose-Built for Databases: While other MCP servers adapt general LLMs for database work, XiYan uses models designed from the ground up for text-to-SQL conversion. The results speak for themselves—it outperforms standard MySQL and PostgreSQL MCP servers by 2-22 percentage points on MCPBench.

Security-First Architecture: Run everything locally if needed. No API keys, no external calls, no data leaving your infrastructure. Perfect for enterprise environments where database queries touching external services are non-starters.

Multiple Deployment Options:

• Remote mode: Use hosted XiYanSQL models via ModelScope or DashScope
• Local mode: Deploy the model on your own infrastructure
• Hybrid: Fall back to OpenAI/Claude when needed

Real Productivity Gains

Instant Schema Understanding: Point XiYan at your database and ask questions like "Show me users who haven't logged in this month" or "What's our revenue breakdown by product category?" It understands your actual table structure, relationships, and data types.

Natural Language Exploration: Instead of memorizing column names and writing JOIN statements, explore your data conversationally. "Which customers have the highest lifetime value?" becomes a simple conversation, not a complex SQL archaeology project.

Reliable Results: Because XiYan uses specialized models, you get consistent, executable SQL. No more debugging hallucinated table names or malformed queries.

Integration That Just Works

XiYan plugs into your existing MCP workflow:

• Claude Desktop: Natural language database queries directly in your AI conversations
• Cline/Cursor: Database exploration without context switching
• Goose: Data analysis as part of your development flow

Setup is straightforward—configure your database connection and model preferences in a YAML file, then XiYan handles the text-to-SQL translation automatically.

Multiple Database Support

Currently supports MySQL and PostgreSQL with more dialects coming. Whether you're working with a local development database or production PostgreSQL cluster, XiYan adapts to your existing infrastructure.

When to Use XiYan

Perfect for: Teams that need reliable database querying through natural language, data analysts who want conversational database exploration, developers building AI applications that need consistent SQL generation.

Skip if: You're only doing basic SELECT statements occasionally, or you need databases beyond MySQL/PostgreSQL right now.

Getting Started

1. Install via pip: pip install xiyan-mcp-server
2. Configure your database and model preferences in config.yml
3. Add to your MCP client configuration
4. Start asking questions about your data

The XiYan team has already done the hard work of training specialized text-to-SQL models and benchmarking them against real-world scenarios. You get to skip the model fine-tuning and prompt engineering—just point it at your database and start querying naturally.

Your databases have stories to tell. XiYan gives you the right language to ask the questions.