XiYan MCP Server

The XiYan MCP Server solves a common bottleneck for developers who want to embed natural‑language data access into AI assistants: translating free‑form queries into precise SQL and executing them against a database. By exposing the XiYanSQL engine as an MCP service, it turns any LLM‑powered assistant into a conversational database client without requiring custom code for query parsing or database drivers. This means that developers can focus on building higher‑level interactions while the server handles the complex, error‑prone task of text‑to‑SQL conversion.

At its core, the server offers a single tool that accepts an arbitrary natural‑language request and returns tabular results. The tool internally invokes the state‑of‑the‑art XiYanSQL model, which has been benchmarked on open text‑to‑SQL datasets and published in recent research. Because the model is served behind an MCP endpoint, it can be used by any client that speaks MCP, such as Claude Desktop, Goose, or Cursor. The server also exposes database metadata through and resources, allowing the assistant to list tables or fetch sample rows for context. This metadata surface is essential for prompt engineering, as it lets the model reason about schema before generating queries.

Key capabilities include:

• Multi‑model support: Switch between general LLMs (GPT, QwenMax) and the dedicated text‑to‑SQL SOTA model through configuration.
• Pure local deployment: Run the entire stack on a single machine, ensuring data never leaves the premises—an important feature for regulated industries.
• Resource listing and sampling: Provide schema information and sample data to the LLM, improving accuracy in complex queries.
• Extensible resource format: The scheme can be extended to other databases with minimal changes.

Typical use cases span data‑driven chatbots, business intelligence dashboards, and automated reporting tools. For example, a sales manager can ask an AI assistant, “Show me the top five customers by revenue in Q3,” and receive a live table without writing SQL. In a compliance setting, the local mode guarantees that sensitive financial data remains on‑premises while still benefiting from advanced language models.

Integration is straightforward: developers add the XiYan MCP Server to their stack, configure the LLM and database endpoints in a YAML file, and point their AI client at the server’s MCP URL. The server then mediates all interactions, translating natural language into SQL, executing queries, and returning results in a JSON format that the assistant can render. This seamless bridge between conversational AI and structured data makes XiYan MCP Server a powerful tool for any developer looking to unlock the value of their databases through natural language.