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 Pydantic AI 7 tools — connect in under 2 minutes

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

ai.pydantic.dev/mcp

Built by Vinkius GDPR 7 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect DataStax Astra DB Vector through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to DataStax Astra DB Vector "
            "(7 tools)."
        ),
    )

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

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

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.

Pydantic AI validates every DataStax Astra DB Vector tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

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

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the DataStax Astra DB Vector MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your DataStax Astra DB Vector integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your DataStax Astra DB Vector connection logic from agent behavior for testable, maintainable code

DataStax Astra DB Vector + Pydantic AI Use Cases

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

Type-safe data pipelines: query DataStax Astra DB Vector with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple DataStax Astra DB Vector tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query DataStax Astra DB Vector and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock DataStax Astra DB Vector responses and write comprehensive agent tests

DataStax Astra DB Vector MCP Tools for Pydantic AI (7)

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

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

DataStax Astra DB Vector + Pydantic AI FAQ

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

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your DataStax Astra DB Vector MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.