How to Use the Typesense Vector Search MCP in Cursor

Q: What is the best way to execute a query with Typesense Vector Search in Cursor?

Use searchvectors, making sure you include the collection name, your text filter, and the vector string. The agent handles stitching this into executable code.

Q: Can I programmatically manage collections using Typesense Vector Search in Cursor?

Yes. You'll use createcollection to define schema or check existing definitions with getcollectiondetails. This gives you full control over the search environment.

Q: Does this MCP Server help me find out what collections exist in Cursor?

Sure thing. The listvectorcollections tool lists everything available in your Typesense instance, so you know exactly which collection to query against.

Write production code that uses real vector search results directly in Cursor.

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Connect Typesense Vector Search MCP to Cursor

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

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Build Code with Typesense Vector Search and Cursor

The `search_vectors` tool lets your AI agent execute a semantic query, returning live data. This response isn't fake; you can write code that uses the exact results to populate variables or build logic. It combines vector similarity search with text filtering, giving you highly specific context right where your cursor is sitting.

Manage Search Indexing in Cursor

To get data into your project, call `index_document`. You provide the collection name and the document JSON. This ensures that any code referencing this search index gets real, up-to-date context. Need to clean house? Use `delete_document` when a record is outdated, guaranteeing your code always works with current information.

Define Search Structure in Cursor

You must define the structure first. Run `create_collection`, passing in the required schema JSON object. This tells the system what fields to expect when it receives data for vector indexing. Also, check out `list_vector_collections` if you're unsure which search index your project needs.

Setup guide

Set up Typesense Vector Search 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 Typesense Vector Search 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 Typesense Vector Search transactions." If the MCP tools are loaded correctly, Cursor will call the Typesense Vector Search tools automatically. You can also check Settings → MCP for a green status indicator.

.cursor/mcp.json

{
  "mcpServers": {
    "typesense-vector-search-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 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 Cursor

The agent calls the MCP tools and injects live API responses directly into the code you're writing. This lets you build real functionality that relies on semantic search results.

Use `search_vectors`, making sure you include the collection name, your text filter, and the vector string. The agent handles stitching this into executable code.

Yes. You'll use `create_collection` to define schema or check existing definitions with `get_collection_details`. This gives you full control over the search environment.

Sure thing. The `list_vector_collections` tool lists everything available in your Typesense instance, so you know exactly which collection to query against.

It deals with structured documents and associated vector embeddings. These are critical for semantic search, allowing your code to process complex, context-aware information.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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