Turso MCP Cloud Server

The mcp‑turso‑cloud server bridges the gap between large language models and Turso’s serverless SQLite platform, giving AI assistants a secure, authenticated gateway to create, manage, and query databases on the fly. By exposing both organization‑level and database‑level operations through MCP, it enables developers to embed persistent data storage directly into conversational flows without leaving the AI environment. This is especially valuable for building knowledge‑base driven agents, personalized recommendation systems, or any application that requires dynamic data retrieval and manipulation within a single prompt.

At its core, the server implements a two‑level authentication scheme. Organization tokens—obtained from the Turso dashboard—authorize high‑level actions such as listing, creating, or deleting databases. Once an organization token is validated, the server automatically generates short‑lived database tokens that grant fine‑grained access to a specific database. This separation not only enforces least‑privilege principles but also simplifies permission management for developers who need to expose multiple databases to different AI agents.

The toolset is split into two logical categories. Organization‑level tools let you enumerate all databases, spin up new ones with custom settings, tear them down, and generate tokens for downstream use. Database‑level tools cover everyday data tasks: listing tables, describing schemas, running read‑only SELECT or PRAGMA queries via , and executing full CRUD operations with . The inclusion of a vector search capability demonstrates Turso’s SQLite extensions, allowing AI assistants to perform similarity searches over embeddings directly within the database.

Security is a primary concern. By distinguishing read‑only queries from destructive ones, the server permits automatic approval for safe operations while requiring explicit review before any data‑modifying statement runs. This design lets developers set up approval workflows or sandbox environments where an AI can propose changes but a human must confirm them, mitigating accidental data loss. The documentation also stresses careful review of SQL before approval, reinforcing best practices in AI‑driven data manipulation.

In practice, developers can integrate this MCP server into any Claude or other LLM workflow that supports MCP. For instance, a customer support bot could query a Turso database to retrieve ticket histories, update status fields, or perform vector similarity searches against knowledge base embeddings—all within a single conversational turn. Similarly, an analytics assistant could spin up temporary databases for exploratory analysis, then delete them when the session ends. The combination of granular authentication, clear tool categorization, and built‑in safety checks makes mcp‑turso‑cloud a robust foundation for AI applications that need reliable, on‑demand data access.