How to Use the Weaviate MCP in VS Code Copilot

Q: How do I verify the Weaviate schema structure using VS Code Copilot?

Use getfullschema to retrieve all collection definitions. This makes sure every developer on your team is working with an accurate map of the data model.

Q: What tools are available for vector search when using Weaviate and VS Code Copilot?

The primary tool is searchnearvector. It requires you to provide a class name and a query vector (JSON array of floats) to find the closest matches in your Weaviate data.

Q: What if I need to list multiple objects in a team setting with VS Code Copilot?

Use listobjects. This function handles basic pagination for a given class, letting your agent retrieve multiple entries without overwhelming the system or hitting limits.

Standardize Weaviate database access across your team using VS Code Copilot's MCP Server tooling.

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Connect Weaviate MCP to VS Code Copilot

Create your Vinkius account to connect Weaviate to VS Code Copilot 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 VS Code Copilot

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Centralize schema definitions with the MCP Server in VS Code Copilot

The `get_full_schema` tool retrieves every collection's structure, allowing your entire team to see what data is available. Similarly, `get_class_schema` isolates the definition for a single class. This consistency means that whether you’re in development or QA, everyone uses the same understanding of the Weaviate database.

Execute vector searches via the MCP Server in VS Code Copilot

`search_near_vector` performs nearest neighbor similarity searches. You pass a class name and a query vector, and your agent gets back relevant data records. This is how you build reliable code that interacts with complex AI databases. It moves beyond basic filtering, finding context based on deep semantic relationships.

Audit Weaviate metadata using the MCP Server in VS Code Copilot

Check instance health and structure using `get_instance_metadata` or `get_cluster_nodes`. These tools give your team a clear picture of the underlying infrastructure. You can also get object data directly via `get_object_details` with just a UUID. It’s essential for maintaining enterprise-grade reliability across the codebase.

Setup guide

Set up Weaviate MCP in VS Code Copilot

Prerequisites

VS Code 1.99 or later with GitHub Copilot extension
Active Vinkius subscription with a valid endpoint token

1

Open MCP configuration

Open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P) and run "MCP: Add Server". Select HTTP (Streamable) as the server type. VS Code will create .vscode/mcp.json in your workspace.
2

Add the Weaviate MCP

Paste the JSON snippet shown on the right into your .vscode/mcp.json. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Switch to Agent mode

Open Copilot Chat (Cmd+Shift+I / Ctrl+Shift+I) and switch to Agent mode using the dropdown. MCP tools are only available in Agent mode — they do not appear in Edit or Ask modes.
4

Verify the connection

In the Copilot Chat input, type # to list available tools. You should see the Weaviate tools listed. Try asking: "List my recent Weaviate transactions" and Copilot will invoke them automatically.

.vscode/mcp.json

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Weaviate. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Weaviate MCP in VS Code Copilot

Use `get_full_schema` to retrieve all collection definitions. This makes sure every developer on your team is working with an accurate map of the data model.

The primary tool is `search_near_vector`. It requires you to provide a class name and a query vector (JSON array of floats) to find the closest matches in your Weaviate data.

Yes. The tools allow you to retrieve specific metadata about any data object by its UUID, helping ensure that only authorized records are accessed and handled.

Use `list_objects`. This function handles basic pagination for a given class, letting your agent retrieve multiple entries without overwhelming the system or hitting limits.

The server interacts with structured data objects within defined classes. These are typically identified by a unique UUID and include associated vector embeddings.

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