YDB MCP Server

The YDB MCP server bridges the gap between large‑language models (LLMs) and Yandex Database (YDB), a distributed, fault‑tolerant SQL engine. By exposing YDB’s full query and schema capabilities through the Model Context Protocol, developers can let an AI assistant read from, write to, and manage their databases using natural language or structured prompts. This eliminates the need for custom API wrappers or manual database tooling when building AI‑powered applications.

At its core, the server translates MCP tool calls into YDB client operations. When an LLM issues a request—such as “fetch the last 10 orders for user 123”—the server parses the command, authenticates against YDB using one of several supported methods (anonymous, login/password, access token, or service account), and executes the query via YDB’s gRPC interface. Results are returned in a JSON‑compatible format that the assistant can embed directly into responses, logs, or further computational steps. The integration is stateless and can run in any environment that supports Python, making it ideal for cloud functions, serverless deployments, or local development.

Key features include:

• Multi‑auth support: Seamlessly switch between anonymous, credentialed, token‑based, or service account authentication without changing the client code.
• Rich query handling: Execute arbitrary SQL, DDL, and transactional statements while preserving YDB’s consistency guarantees.
• Schema introspection: Tools for listing tables, columns, and indexes enable AI assistants to auto‑generate documentation or suggest migrations.
• Extensible configuration: The server can be launched via , , or plain , allowing quick adoption in diverse tooling pipelines.

Typical use cases span from conversational data exploration—where a user can ask the assistant to “show me sales trends for last quarter”—to automated data pipelines, where an LLM orchestrates ETL jobs by invoking YDB queries as part of a larger workflow. In research environments, the server enables rapid prototyping of AI‑driven analytics without writing boilerplate database code.

By integrating YDB MCP into an LLM workflow, developers gain a single, consistent interface for database interaction. The server abstracts low‑level connection details, supports secure authentication, and returns results in a format that can be directly consumed by AI agents. This streamlines development, reduces operational overhead, and unlocks powerful data‑centric capabilities for next‑generation conversational applications.