How to Use the Vertex AI Vector Search MCP in Pydantic AI

Q: How do I use Vertex AI Vector Search with Pydantic AI?

Your agent uses the searchnearestneighbors tool, and Pydantic validates the response structure. This means you always receive predictable, correctly typed data for your application logic.

Q: Does Pydantic AI support listing all indexes with Vertex AI Vector Search?

Yes. The listvectorindexes tool provides the list, and because the response is validated by Pydantic, you don't have to worry about unexpected JSON fields.

Q: Can I use Vertex AI Vector Search with Pydantic AI for operational tracking?

You can. The listvectoroperations tool tracks long-running jobs, and Pydantic ensures that the operation status details you receive are correctly formatted.

Guarantee correctness: Validate every Vector Search result using Pydantic AI.

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Connect Vertex AI Vector Search MCP to Pydantic AI

Create your Vinkius account to connect Vertex AI Vector Search 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 Vertex AI Vector Search with Pydantic AI

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Validate Nearest Neighbors with MCP Server

The `search_nearest_neighbors` tool executes the vector search, returning results that your agent validates. You provide an endpoint ID, index ID, and a query vector in JSON. Pydantic ensures that even if the underlying API changes, your agent fails loudly with a validation error, never silently corrupting data.

Get Index Configuration Details

Use `get_index_details` to retrieve metadata and configuration for any specific vector index. This structured output is immediately validated by Pydantic. You get reliable, type-safe parameters before your agent even attempts a search.

List All Vector Indexes in the Project

The `list_vector_indexes` tool provides all indexes and their basic metadata. Because Pydantic validates the response, you are guaranteed that the list structure is correct every time. This prevents runtime failures when gathering an inventory of available search sources.

Setup guide

Set up Vertex AI Vector Search 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": {
        "vertex-ai-vector-search-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Vertex AI Vector Search transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vertex AI Vector Search. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Vertex AI Vector Search MCP in Pydantic AI

Your agent uses the `search_nearest_neighbors` tool, and Pydantic validates the response structure. This means you always receive predictable, correctly typed data for your application logic.

The server handles vector embedding data, index configurations, and query vectors. Pydantic adds a layer of validation on top of this sensitive operational data structure.

Yes. The `list_vector_indexes` tool provides the list, and because the response is validated by Pydantic, you don't have to worry about unexpected JSON fields.

You can. The `list_vector_operations` tool tracks long-running jobs, and Pydantic ensures that the operation status details you receive are correctly formatted.

The primary data types touched include index metadata (strings/objects), operational status records, and high-dimensional query vectors.

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