How to Use the Zilliz Cloud MCP in Pydantic AI

Q: Can I manage multiple collections using Zilliz Cloud and Pydantic AI?

Yes. You use listcollections to get a list of all available groups. Then you can target any specific collection with tools like createcollection or dropcollection.

Q: If I run a search query, what tools are involved when using Zilliz Cloud and Pydantic AI?

The main tool is searchvectors. Depending on the request scope, it also calls queryentities to filter results by metadata before running the final vector similarity check.

Q: If I use insertentities, what data type does Zilliz Cloud handle?

The server handles vector and entity records. The insertentities tool takes structured input, which means it expects clean, typed data for every record.

Guarantee correct data structures when querying Zilliz Cloud with Pydantic AI.

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Connect Zilliz Cloud MCP to Pydantic AI

Create your Vinkius account to connect Zilliz Cloud 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 Zilliz Cloud with Pydantic AI

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Vector Search & Retrieval

The tool `search_vectors` executes vector similarity searches. It requires a JSON search configuration, and crucially, the resulting data is validated against your defined models. You get reliable results because Pydantic AI ensures that whatever Zilliz Cloud returns actually matches the fields you expect.

Managing Collections

Use `list_collections` to map out all collections available in the cluster. Need details? Call `describe_collection`. You can then create new storage areas with `create_collection`. To clean up, just run `drop_collection`.

Data Ingestion

When adding data, call `insert_entities` to populate your collections. This process is validated right away. If you need the full dataset in memory for deep analysis, use `load_collection`.

Setup guide

Set up Zilliz Cloud 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": {
        "zilliz-cloud-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Zilliz Cloud transactions")
print(result.output)

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Common questions about Zilliz Cloud MCP in Pydantic AI

The agent validates every response against your Pydantic model at runtime. If Zilliz Cloud sends unexpected fields, the process fails with a clear error instead of allowing bad data to pass through.

This server touches vector and entity data. The use of Pydantic AI adds a layer of schema enforcement, ensuring that even if the underlying zilliz-cloud-mcp returns sensitive information, it's structured correctly for your application.

Yes. You use `list_collections` to get a list of all available groups. Then you can target any specific collection with tools like `create_collection` or `drop_collection`.

The main tool is `search_vectors`. Depending on the request scope, it also calls `query_entities` to filter results by metadata before running the final vector similarity check.

The server handles vector and entity records. The `insert_entities` tool takes structured input, which means it expects clean, typed data for every record.

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