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 LangChain 7 tools — connect in under 2 minutes

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

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect DataStax Astra DB Vector through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "datastax-astra-db-vector": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using DataStax Astra DB Vector, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

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.

LangChain's ecosystem of 500+ components combines seamlessly with DataStax Astra DB Vector through native MCP adapters. Connect 7 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

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

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 7 tools from DataStax Astra DB Vector via MCP

Why Use LangChain with the DataStax Astra DB Vector MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine DataStax Astra DB Vector MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across DataStax Astra DB Vector queries for multi-turn workflows

DataStax Astra DB Vector + LangChain Use Cases

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

RAG with live data: combine DataStax Astra DB Vector tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query DataStax Astra DB Vector, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain DataStax Astra DB Vector tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every DataStax Astra DB Vector tool call, measure latency, and optimize your agent's performance

DataStax Astra DB Vector MCP Tools for LangChain (7)

These 7 tools become available when you connect DataStax Astra DB Vector to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

DataStax Astra DB Vector + LangChain FAQ

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

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.