How to Use the Weaviate MCP in Claude Code

Q: How do I use Weaviate for vector search in CI/CD?

You execute searchnearvector directly from the terminal. Pass the class name and your query vector as a JSON array of floats to get results that can be piped into subsequent shell commands.

Q: What is the best way to check schema structure with Weaviate and Claude Code?

Run getfullschema first. This gives you a complete inventory of all collections in your instance, helping you map out dependencies for your script.

Q: Does this MCP Server handle data listing for automation?

Yes, the listobjects tool supports basic pagination via limit. This is useful when your automated job needs to process records in batches rather than querying by ID.

Q: What kind of operational checks can I run with Weaviate and Claude Code?

You can check the cluster health using getclusternodes. This confirms that all necessary components are online before your pipeline attempts to read data.

Run reliable vector queries in CI/CD pipelines using Claude Code and MCP Server.

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Connect Weaviate MCP to Claude Code

Create your Vinkius account to connect Weaviate to Claude Code 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 Claude Code

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Vector Similarity Search for Scripting with Claude Code

The core function is `search_near_vector`. You pipe a class name and the query vector as a JSON array of floats directly into your shell script. The output provides machine-readable results, perfect for automated pipelines.

Schema Dumping via Claude Code MCP Server

Need to write a migration script? Use `get_full_schema` to dump the entire structure of every collection into JSON. For targeted checks, `get_class_schema` retrieves definitions for just one specific class.

Getting Instance Details using Claude Code

Debugging CI/CD jobs requires context. You can run `get_instance_metadata` to grab high-level system status, or pinpoint a record by calling `get_object_details` with its UUID.

Setup guide

Set up Weaviate MCP in Claude Code

Prerequisites

Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
Active Vinkius subscription with a valid endpoint token

1

Run the add command

Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.
2

Verify the connection

Start a Claude Code session and type /mcp to list connected servers. You should see weaviate-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest Weaviate transactions." It will automatically discover and invoke the available Weaviate tools.

Terminal

claude mcp add --transport http weaviate-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Get your connection token →

Prerequisites

Claude Code CLI installed
Active Vinkius subscription with a valid endpoint token

1

Open the config file

Create or edit .mcp.json in your project root for project-level scope, or ~/.claude.json for user-level scope.
2

Add the Weaviate MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Code

Start a new Claude Code session. Type /mcp to confirm the server is connected. The tools will be automatically available in your conversation.

.mcp.json

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

Get your connection token →

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Common questions about Weaviate MCP in Claude Code

You execute `search_near_vector` directly from the terminal. Pass the class name and your query vector as a JSON array of floats to get results that can be piped into subsequent shell commands.

Run `get_full_schema` first. This gives you a complete inventory of all collections in your instance, helping you map out dependencies for your script.

Yes, the `list_objects` tool supports basic pagination via limit. This is useful when your automated job needs to process records in batches rather than querying by ID.

You can check the cluster health using `get_cluster_nodes`. This confirms that all necessary components are online before your pipeline attempts to read data.

The server works with schema definitions, object UUIDs, and vector embeddings. The `get_instance_metadata` call provides general information about the database setup itself.

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