How to Use the Zilliz Cloud MCP in Claude Code

Q: How do I query data with Zilliz Cloud in Claude Code?

You execute the searchvectors tool, providing a JSON search configuration. The output is standard text that you can process further in your shell script.

Q: What collections are available via Zilliz Cloud with Claude Code?

The listcollections tool provides an API endpoint listing every vector collection managed by the server, allowing you to programmatically check availability.

Q: Can I apply metadata filters using Zilliz Cloud in Claude Code?

Yes. The queryentities tool lets your script filter results based on specific data attributes before running the vector search, which is crucial for robust automation.

Q: Does Zilliz Cloud support schema changes with Claude Code?

You can manage schemas by calling dropcollection to wipe an old store or using createcollection to establish a brand-new, clean vector collection structure.

Headless vector search for automated CI/CD pipelines using Claude Code.

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Connect Zilliz Cloud MCP to Claude Code

Create your Vinkius account to connect Zilliz Cloud 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 Zilliz Cloud with Claude Code

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Claude

Perplexity

Automate knowledge retrieval in scripts.

Use `search_vectors` to query external documentation directly within your shell script. You pass the JSON search configuration, and the output is piped straight into subsequent commands. If you need to check related items based on metadata, `query_entities` runs those filters first, making sure your automated process gets highly accurate data.

Manage vector collections in CI/CD.

The agent can provision a new store using `create_collection`. This is critical for pipelines that need isolated environments to run tests against. When the job finishes, you don't want clutter. The script can use `drop_collection` to clean up the resources immediately afterward.

Bulk data handling in cron jobs.

Use `insert_entities` to batch-load data into a collection as part of your job routine. This is ideal for scheduled updates or nightly syncs. For speed, the agent can also use `load_collection` and `release_collection`, ensuring memory isn't leaked across successive script runs.

Setup guide

Set up Zilliz Cloud 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 zilliz-cloud-mcp with a green status indicator.
3

Start using tools

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

Terminal

claude mcp add --transport http zilliz-cloud-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 Zilliz Cloud 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": {
    "zilliz-cloud-mcp": {
      "type": "url",
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

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Common questions about Zilliz Cloud MCP in Claude Code

You execute the `search_vectors` tool, providing a JSON search configuration. The output is standard text that you can process further in your shell script.

The `list_collections` tool provides an API endpoint listing every vector collection managed by the server, allowing you to programmatically check availability.

Yes. The `query_entities` tool lets your script filter results based on specific data attributes before running the vector search, which is crucial for robust automation.

You can manage schemas by calling `drop_collection` to wipe an old store or using `create_collection` to establish a brand-new, clean vector collection structure.

The server handles vector collections. Your scripts interact with vectors and metadata; always restrict the scope of the job to only necessary data types to maintain compliance.

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