How to Use the Weaviate MCP in Pydantic AI

Q: How do I use Weaviate and Pydantic AI to find related documents?

You execute searchnearvector. The result is automatically validated against your Python models. This means the data feeding into your agent's decision-making process is guaranteed correct.

Q: Does Pydantic AI validate Weaviate schema definitions?

Yes, using getfullschema or getclassschema populates data that gets type-checked. This ensures your agent's understanding of the database structure is always valid.

Q: How does Pydantic AI handle listing Weaviate objects?

When you use listobjects, every single record returned must pass Pydantic validation. If a field is missing or misspelled, the agent stops and alerts you.

Q: What tools are available to check the operational status of Weaviate?

You can use getinstancemetadata for general instance health checks, and getclusternodes gives details on individual node operations.

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Connect Weaviate MCP to Pydantic AI

Create your Vinkius account to connect Weaviate 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 Weaviate with Pydantic AI

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Claude

Perplexity

Vector Similarity Search

When your agent runs `search_near_vector`, the response is immediately validated. You supply a class name and a query vector array, and if the returned data doesn't match the expected Pydantic model, the agent fails loudly. This guarantees that even complex AI actions only process correctly structured context from Weaviate.

Data Integrity Checks

Want to validate the source schema? `get_full_schema` pulls all definitions, and `get_class_schema` handles single collections. If weaviate returns unexpected metadata structure, Pydantic AI catches it before your agent sees garbage data. This prevents silent corruption that usually plagues complex multi-model workflows.

Object Verification

The `get_object_details` tool retrieves a specific record by UUID. Because Pydantic AI validates the output, your agent knows exactly what fields to expect and can't proceed if data is malformed. It also supports basic enumeration via `list_objects`, ensuring every object returned adheres to its schema.

Setup guide

Set up Weaviate 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": {
        "weaviate-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Weaviate MCP in Pydantic AI

You execute `search_near_vector`. The result is automatically validated against your Python models. This means the data feeding into your agent's decision-making process is guaranteed correct.

Yes, using `get_full_schema` or `get_class_schema` populates data that gets type-checked. This ensures your agent's understanding of the database structure is always valid.

When you use `list_objects`, every single record returned must pass Pydantic validation. If a field is missing or misspelled, the agent stops and alerts you.

You can use `get_instance_metadata` for general instance health checks, and `get_cluster_nodes` gives details on individual node operations.

This server touches object UUIDs and vector float arrays. These are the primary identifiers and context inputs that require strict validation to maintain high data integrity.

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