How to Use the Typesense Vector Search MCP in Windsurf

Q: How do I perform a Typesense Vector Search using Windsurf?

You use searchvectors. You provide the collection name, your desired text query, and the vector string. Cascade handles the rest of the execution.

Q: Can Windsurf manage my data schema for Typesense Vector Search?

Yep. The createcollection tool lets you define a new collection's JSON schema upfront. You control exactly what fields and types are available in the search index.

Q: What happens when I run a complex Typesense Vector Search with Windsurf?

Windsurf executes searchvectors. It runs the vector query against the indexed documents while simultaneously applying any text filters you specify. You get results that are both semantically relevant and contextually filtered.

Q: Does this MCP Server handle document deletion?

Yes, if a record is bad or outdated, use deletedocument. Be warned: this action permanently removes the data by its ID. You can't undo it.

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Connect Typesense Vector Search MCP to Windsurf

Create your Vinkius account to connect Typesense Vector Search to Windsurf 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 Typesense Vector Search with Windsurf

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Manage search collections easily

You start by calling `list_vector_collections` to see every index Typesense has running. Then, if you need a new one, use `create_collection` and feed it the exact JSON schema details.

Index or update documents automatically

When data changes, just call `index_document`. This tool adds fresh records or updates existing ones in your chosen collection. It keeps your search corpus current without manual intervention. This process is key for maintaining a reliable source of truth; it ensures that any semantic query you run later has the most up-to-date information.

Execute complex vector similarity searches

Want to find related data? You execute `search_vectors`, giving it a collection name, text filter, and your specific vector query string. This combines semantic matching with traditional filtering. It's powerful because you don't have to run two separate tools; the single call handles both the vector similarity search and any necessary text-based constraints.

Setup guide

Set up Typesense Vector Search MCP in Windsurf

Prerequisites

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

1

Open MCP configuration

Click the Cascade assistant icon in the sidebar, then click the hammer icon (🔨) at the top of the panel. Select "Configure" to open ~/.codeium/windsurf/mcp_config.json.
2

Add the Typesense Vector Search 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

Refresh MCPs

Go back to the hammer icon (🔨) in Cascade and click "Refresh". Windsurf will detect the new server. No full restart is needed — the connection is hot-reloaded.
4

Verify in Cascade

Start a new Cascade conversation and ask something like "Show my Typesense Vector Search payment history." If connected, Cascade will call the Typesense Vector Search tools directly. You will see a green dot next to the server name in the MCP panel.

mcp_config.json

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Typesense Vector Search. 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 Typesense Vector Search MCP in Windsurf

You use `search_vectors`. You provide the collection name, your desired text query, and the vector string. Cascade handles the rest of the execution.

Yep. The `create_collection` tool lets you define a new collection's JSON schema upfront. You control exactly what fields and types are available in the search index.

Windsurf executes `search_vectors`. It runs the vector query against the indexed documents while simultaneously applying any text filters you specify. You get results that are both semantically relevant and contextually filtered.

Yes, if a record is bad or outdated, use `delete_document`. Be warned: this action permanently removes the data by its ID. You can't undo it.

This server works with structured document metadata and vector embeddings, which are stored as JSON objects within various collections. The actual content is the indexed document data.

Use it with your favorite AI tools

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