Doris MCP Server

Overview of Doris‑MCP‑Lite

Doris‑MCP‑Lite is a lightweight Model Context Protocol (MCP) server that bridges large language models with Apache Doris and any MySQL‑compatible database. By exposing the database schema, metadata, and read‑only query capabilities through a standardized MCP interface, it lets AI assistants discover and interrogate data sources without custom code. This solves the common developer pain point of wiring an LLM to a relational database: schema discovery, query generation, and result interpretation are all handled through the MCP contract, freeing developers to focus on business logic rather than low‑level integration.

The server’s core value lies in its contextual awareness. It publishes database tables, columns, and relationships as structured resources that the LLM can reference during conversation. When a user asks an analytics question, the assistant can automatically pull in the relevant table definitions and generate a precise SQL statement. The server then executes the query asynchronously, returning results in JSON format that can be directly embedded into responses. This end‑to‑end flow—schema exposure, prompt assistance, query execution—provides a seamless, secure path for AI‑driven data exploration.

Key capabilities include:

• Read‑only SQL execution with connection pooling and asynchronous support, ensuring efficient use of database resources even under high concurrency.
• Metadata querying for schemas, tables, and resource usage, enabling the LLM to understand the structure of the data before formulating queries.
• Built‑in prompt templates for analytics and multi‑role interactions, allowing developers to fine‑tune how the assistant frames questions and interprets results.
• User‑defined SQL analysis prompts that can be tailored to specific business domains or compliance requirements.
• Resource exposure of the database schema as MCP resources, giving the assistant a rich context for generating accurate queries.

In practice, Doris‑MCP‑Lite shines in scenarios such as data‑driven dashboards, conversational analytics bots, and automated reporting pipelines. A marketing team can ask the assistant for “monthly churn rates” and receive a polished answer that includes both the raw data and an explanatory narrative. An operations engineer can query “resource usage by table” to spot bottlenecks, while a data scientist can request trend analyses that the server automatically translates into efficient SQL. Because the server adheres to MCP, any LLM client that supports the protocol—Claude, GPT‑4o, or custom agents—can plug in instantly without rewriting adapters.

What sets Doris‑MCP‑Lite apart is its focus on performance and developer ergonomics. Connection pooling, asynchronous execution, and a minimal dependency footprint make it suitable for both local development and production deployments. Its early‑stage, open‑source nature invites contributions, while the built‑in prompt library provides a head start for teams building conversational analytics solutions. In short, Doris‑MCP‑Lite turns a traditional relational database into an AI‑friendly knowledge base with minimal friction.