How to Use the DataStax Astra DB Vector MCP in Pydantic AI

Enforce strict schema validation for DataStax Astra DB Vector using Pydantic AI.

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Connect DataStax Astra DB Vector MCP to Pydantic AI

Create your Vinkius account to connect DataStax Astra DB Vector to Pydantic AI 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 DataStax Astra DB Vector with Pydantic AI

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Perplexity

Type-safe document management for Pydantic AI

Every response from `find_documents` is validated against your Pydantic models at runtime. If the database returns a malformed record, the agent catches it before processing. This prevents silent errors where your agent might otherwise hallucinate field names. You get absolute certainty that the data entering your logic is structured correctly.

Search and validate with Pydantic AI

Execute `vector_search` and let the framework verify the incoming vector metadata. Your agent works with strictly typed objects rather than loose dictionaries. This keeps your application logic clean and predictable. If a field is missing or the wrong type, the agent fails immediately with a clear validation error.

Sync database records in Pydantic AI

Use `insert_document` to push records that match your predefined schemas. The agent ensures that every field meets your constraints before the write occurs. `count_documents` also returns typed integers for your internal reporting. You maintain strict control over your data integrity throughout the entire application flow.

Setup guide

Set up DataStax Astra DB Vector MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "datastax-astra-db-vector-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to DataStax Astra DB Vector tools.",
)

result = await agent.run("List recent DataStax Astra DB Vector transactions")
print(result.output)

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Common questions about DataStax Astra DB Vector MCP in Pydantic AI

If the MCP server returns data that violates your Pydantic model, the framework raises a validation error. This stops the agent from using corrupted data.

You define your Pydantic models in your code. The framework then validates the tool output against those models automatically.

Yes, all seven tools are available. You simply add the MCPToolset to your agent configuration.

The server must run as an external process. You connect to it via HTTP or SSE using the MCPToolset class.

All document objects are validated upon receipt to prevent injection. Your data remains protected by the Vinkius zero-trust architecture, ensuring only your agent can trigger these tools.

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