Can my AI agent do similarity searches across vector embeddings?

Yes. Ask the agent to find documents related to a specific vector array in your target collection. The agent natively passes the numerical array directly into Astra DB's ANN engine, instantly returning the top semantically matched documents.

Does this work like standard Cassandra or is it strictly vector-only?

Both. While it excels at vector searches, the integration fully supports standard NoSQL JSON documents. You can insert, find, count, and delete standard text documents using strict JSON filters just like regular database operations.

Can I switch seamlessly between different collections?

Absolutely. Just mention the target collection by name in your prompts, and the agent adapts flawlessly. If you forget which ones exist, you can instruct it to list all available collections within your default namespace anytime.

DataStax Astra DB Vector MCP Server for LlamaIndex 7 tools — connect in under 2 minutes

Name: DataStax Astra DB Vector
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add DataStax Astra DB Vector as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to DataStax Astra DB Vector. "
            "You have 7 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in DataStax Astra DB Vector?"
    )
    print(response)

asyncio.run(main())

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About DataStax Astra DB Vector MCP Server

Connect your Astra DB instance to any AI agent and seamlessly execute complex NoSQL and vector searches through natural conversation. Built on DataStax's powerful engine, this integration gives your AI agents full contextual access to your unstructured data layer.

LlamaIndex agents combine DataStax Astra DB Vector tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Vector Search — Perform Approximate Nearest Neighbor (ANN) similarity searches directly within your chat to find semantically related documents
Document Management — Insert, discover, read, count, or delete exact NoSQL JSON documents across your active collections
Collections — List and browse available tables and collections currently active in your configured Astra DB namespace

The DataStax Astra DB Vector MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect DataStax Astra DB Vector to LlamaIndex via MCP

Follow these steps to integrate the DataStax Astra DB Vector MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 7 tools from DataStax Astra DB Vector

Why Use LlamaIndex with the DataStax Astra DB Vector MCP Server

LlamaIndex provides unique advantages when paired with DataStax Astra DB Vector through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine DataStax Astra DB Vector tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain DataStax Astra DB Vector tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query DataStax Astra DB Vector, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what DataStax Astra DB Vector tools were called, what data was returned, and how it influenced the final answer

DataStax Astra DB Vector + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the DataStax Astra DB Vector MCP Server delivers measurable value.

Hybrid search: combine DataStax Astra DB Vector real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query DataStax Astra DB Vector to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying DataStax Astra DB Vector for fresh data

Analytical workflows: chain DataStax Astra DB Vector queries with LlamaIndex's data connectors to build multi-source analytical reports

DataStax Astra DB Vector MCP Tools for LlamaIndex (7)

These 7 tools become available when you connect DataStax Astra DB Vector to LlamaIndex via MCP:

count_documents

Count total documents in an Astra DB collection

delete_document

Delete a document from an Astra DB collection

find_documents

Useful for standard NoSQL document retrieval. Find documents in an Astra DB collection

find_one_document

Find a single document in an Astra DB collection

insert_document

The document can include a pre-generated $vector key for embedding searches. Insert a new document into an Astra DB collection

list_collections

List all collections in the Astra DB namespace

vector_search

Perform an ANN vector similarity search on an Astra DB collection

Example Prompts for DataStax Astra DB Vector in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with DataStax Astra DB Vector immediately.

"List the collections available in my Astra DB."

"Count the documents inside the 'products' collection."

"Find documents matching this filter in 'user_vectors': {"city": "San Francisco"}."

Troubleshooting DataStax Astra DB Vector MCP Server with LlamaIndex

Common issues when connecting DataStax Astra DB Vector to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

DataStax Astra DB Vector + LlamaIndex FAQ

Common questions about integrating DataStax Astra DB Vector MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query DataStax Astra DB Vector tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.