Astra DB MCP Server

The Astra DB MCP Server extends a large language model’s abilities by turning the cloud‑based Astra DB into a first‑class external data source. Instead of hard‑coding database queries or exposing raw HTTP endpoints, developers can simply instruct an AI assistant to “create a collection,” “list records,” or “find distinct values.” The server translates those high‑level intents into the appropriate Astra DB API calls, returning structured JSON that the assistant can incorporate directly into its responses. This abstraction saves time, reduces boilerplate, and keeps sensitive credentials out of the model’s prompt.

A key value proposition is its seamless integration with popular MCP‑enabled tools such as Claude Desktop and Cursor. By adding a single configuration block, an assistant can discover the server in its tool list and invoke any of the exposed operations. The server’s design follows the MCP “resource, tool, prompt” pattern: each operation is a distinct tool that the model can call on demand. This modularity allows developers to expose only the functions they need, keeping the assistant’s context lean and focused.

The server offers a rich set of capabilities that cover both schema management and data manipulation. Collection‑level tools let you enumerate, create, update, or delete collections—complete with optional vector support for similarity search. Record‑level tools provide CRUD operations, pagination, and field‑based queries such as or . An estimation tool gives quick insight into collection size without a full scan. All tools return JSON, making downstream processing trivial for the assistant or any consuming application.

Real‑world scenarios that benefit from this server include data‑driven product recommendations, real‑time analytics dashboards, or conversational agents that need to pull user profiles from a managed database. For example, an e‑commerce assistant could ask the model to “list all products in the ‘electronics’ collection where price < $200,” and receive a neatly formatted response without writing any code. In an internal knowledge‑base setting, the assistant could index documents into Astra DB and later retrieve them via vector similarity searches, all orchestrated through MCP calls.

Because the server runs locally (via or a command wrapper) and relies on environment variables for authentication, developers retain full control over credentials. Optional keyspace configuration lets teams isolate data per environment or tenant without code changes. The combination of minimal setup, robust tooling, and tight integration with MCP clients makes the Astra DB server a powerful bridge between LLMs and modern cloud databases.