How to Use the Typesense Vector Search MCP in LlamaIndex

Q: How do I query historical documents with Typesense Vector Search and LlamaIndex?

You call searchvectors, specifying the collection name, text query, and vector string. This allows your RAG app to pull relevant context from past indexed data.

Q: Can I add or update documents in my index using Typesense Vector Search and LlamaIndex?

Yes. You use indexdocument, providing the specific collection name and the full document data as a JSON object for ingestion.

Q: What if I need to check what collections are available before querying with Typesense Vector Search and LlamaIndex?

You run listvectorcollections. This tool provides a clear list of all existing collections in the instance, letting you pinpoint your target data source.

Q: If I delete a document using this MCP Server, what information is lost for LlamaIndex?

The deletedocument action permanently removes the record and its associated vector from the collection. This context is immediately unavailable to the RAG system.

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Connect Typesense Vector Search MCP to LlamaIndex

Create your Vinkius account to connect Typesense Vector Search to LlamaIndex 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 LlamaIndex

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Ground answers in live API data with LlamaIndex.

The `search_vectors` tool executes a vector similarity search combined with text filtering. This means your RAG application gets real, grounded results that come from the MCP Server's indexed documents. You combine static knowledge with fresh, queryable data every time you run an agent.

Define structured data sources for LlamaIndex.

Before indexing anything, you gotta set up the container. Use `create_collection` to define the schema details as a JSON object. This gives your knowledge base a strict structure. LlamaIndex relies on this defined architecture to ingest and query information correctly.

Manage document lifecycle with LlamaIndex.

If a source document changes or is archived, you need to clean up. `delete_document` permanently removes the record using its ID. Keeping the index current is critical for reliable semantic search results.

Setup guide

Set up Typesense Vector Search MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Typesense Vector Search MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Typesense Vector Search tools.",
)
response = await agent.run("List recent Typesense Vector Search data")

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Common questions about Typesense Vector Search MCP in LlamaIndex

You call `search_vectors`, specifying the collection name, text query, and vector string. This allows your RAG app to pull relevant context from past indexed data.

It deals with structured document records, including the associated metadata and vectors, all defined by the collection schema you create.

Yes. You use `index_document`, providing the specific collection name and the full document data as a JSON object for ingestion.

You run `list_vector_collections`. This tool provides a clear list of all existing collections in the instance, letting you pinpoint your target data source.

The `delete_document` action permanently removes the record and its associated vector from the collection. This context is immediately unavailable to the RAG system.

Use it with your favorite AI tools

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