MyScale (SQL Vector Database API) MCP. Combine SQL queries with high-recall vector searches.

You gotta manage high-performance vector databases, but you don't wanna write some arcane API calls just to run a query. MyScale lets your AI agent handle complex vector data using standard SQL syntax. Think of it like this: you talk to your agent how you always have—with SQL—and it handles the deep database magic under the hood.

First, you gotta make sure everything's talking. You start by pinging the cluster with ping_cluster. That confirms your credentials are good and that the whole MyScale system is actually up and ready to take connections. Once that green light pops up, you’re cleared for action.

When it comes to querying data, it's straightforward. If you need to run any custom SQL—whether it's a simple SELECT, an INSERT statement, or even an ALTER command—you just call execute_sql_query. The thing here is that no matter what query you throw at it, the results come back formatted as clean JSON.

It makes parsing those data sets a breeze for your agent.

But standard queries aren't enough when you're dealing with meaning. You need semantic search. That’s where vector_search comes in play. You feed it a vector embedding, and the tool automatically builds and runs complex SQL to find content that's semantically similar to what you're looking for. It does all the distance calculation stuff so you don't have to.

If your data structure is missing something, you can build it out. You use create_vector_table when you need a brand new database table set up specifically to handle vector types and metadata constraints. This function lets you define that entire schema—the vector column and all the supporting info—straight from your prompt.

When you've got data in place, you gotta optimize it for speed. You can add specialized performance indexes using create_vector_index. You tell it to build an index like HNSW or ScaNN on an existing table, which dramatically speeds up how fast the database searches huge amounts of data. If you run that and need to know if it's done yet, you check its status with check_index_status.

This tells you instantly whether the index is 'Built,' still 'InProgress,' or if something went wrong ('Error').

So, you validate connectivity first; then, you build your tables and indexes; next, you run queries using plain SQL or perform deep similarity searches. The whole process—from basic data lookup to complex vector indexing—runs through the same familiar SQL syntax your agent knows. You never have to leave the chat window to manage your entire RAG pipeline.