How to Use the Weaviate MCP in Cursor

Q: How do I get a list of all Weaviate collections for my project using Cursor?

You call getfullschema. It retrieves the complete schema, giving you an inventory of every class defined in your Weaviate instance. This is key when structuring new code.

Q: If I write a function using Weaviate and Cursor, how do I ensure it's working with real data?

The agent executes the MCP tools live. When you use searchnearvector, the returned vector data is injected directly into your code block, guaranteeing accurate context.

Q: When testing vector similarity searches with Weaviate and Cursor, what inputs are required?

The searchnearvector tool requires two things: a target class name and the query vector, which must be provided as a JSON array of floats.

Inject Weaviate's structured data context into your code using Cursor's Agent mode.

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Connect Weaviate MCP to Cursor

Create your Vinkius account to connect Weaviate to Cursor and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Weaviate with Cursor

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Use the MCP Server for live database interaction in Cursor

The `search_near_vector` tool lets the AI agent run a nearest neighbor search and inject the resulting data directly into your code. You don't get mock results; you get real, production-grade context. This capability means you can write functions that reference live Weaviate data without ever leaving Cursor.

Inspect Weaviate schemas and metadata in Cursor

Need to know the structure of your database? `get_full_schema` pulls all collection definitions. You can also use `get_class_schema` on a specific class to verify field names before writing an API call. These schema tools ensure the code generated by the AI agent is syntactically correct for Weaviate.

Retrieve object details and manage MCP Server data in Cursor

`get_object_details` lets you grab a specific record using its UUID, which is perfect for populating variables. You can also `list_objects`, paginating through results when the dataset is large. These basic CRUD operations give your agent granular control over the data context in your editor.

Setup guide

Set up Weaviate MCP in Cursor

Prerequisites

Cursor installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP Settings

Go to Cursor Settings → MCP or open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and search for "MCP: Add Server".
2

Add the Weaviate MCP

Cursor will create or open .cursor/mcp.json in your project root. Paste the JSON snippet on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Enable Agent mode

Open Composer (Cmd+I / Ctrl+I) and switch to Agent mode using the dropdown at the top. MCP tools are only available in Agent mode.
4

Verify the connection

Ask Cursor something like "List my recent Weaviate transactions." If the MCP tools are loaded correctly, Cursor will call the Weaviate tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "weaviate-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Weaviate MCP in Cursor

You call `get_full_schema`. It retrieves the complete schema, giving you an inventory of every class defined in your Weaviate instance. This is key when structuring new code.

It pulls operational details for the entire cluster. Your agent uses this to verify that all nodes are active and functioning before attempting any data queries.

The agent executes the MCP tools live. When you use `search_near_vector`, the returned vector data is injected directly into your code block, guaranteeing accurate context.

It handles structured data records identified by UUIDs. The tools allow you to list these specific data objects within any given class name.

The `search_near_vector` tool requires two things: a target class name and the query vector, which must be provided as a JSON array of floats.

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