Can I perform a vector search using float arrays through the agent?

Yes. The `search_near_vector` tool allows you to perform semantic searches by providing a query vector as a JSON array of floats. Your AI agent will return the most similar objects from your Weaviate collection.

How do I see the data structure of my Weaviate collections?

You can use the `get_full_schema` tool to see all classes and properties defined in your instance, or `get_class_schema` if you want to focus on a specific collection's definition.

Is it possible to monitor cluster health via chat?

Absolutely. Use the `get_cluster_nodes` tool to retrieve operational data for all nodes in your Weaviate cluster, including their current status and resource utilization metrics.

Weaviate MCP Server for Pydantic AI 7 tools — connect in under 2 minutes

Name: Weaviate
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 Weaviate 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 Weaviate "
            "(7 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Weaviate?"
    )
    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 Weaviate MCP Server

Connect your Weaviate instance to any AI agent and harness the power of vector search and semantic data management through natural conversation.

Pydantic AI validates every Weaviate 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

Semantic Search — Perform nearest neighbor vector similarity searches to find relevant content based on context and meaning
Schema Management — Retrieve the complete instance schema or specific class definitions to understand your data structure
Object Discovery — Browse and list data objects within any class, including full property values and vector data
Deep Data Audit — Retrieve specific data objects by their UUID to inspect metadata and internal configurations
Cluster Monitoring — Monitor operational health, node status, and resource usage of your Weaviate cluster nodes
Instance Metadata — View server version, enabled modules, and high-level configuration details directly from your agent

The Weaviate 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 Weaviate to Pydantic AI via MCP

Follow these steps to integrate the Weaviate 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 Weaviate with type-safe schemas

Why Use Pydantic AI with the Weaviate MCP Server

Pydantic AI provides unique advantages when paired with Weaviate 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 Weaviate integration code

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

Dependency injection system cleanly separates your Weaviate connection logic from agent behavior for testable, maintainable code

Weaviate + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Weaviate MCP Server delivers measurable value.

Type-safe data pipelines: query Weaviate with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Weaviate tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Weaviate and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Weaviate responses and write comprehensive agent tests

Weaviate MCP Tools for Pydantic AI (7)

These 7 tools become available when you connect Weaviate to Pydantic AI via MCP:

get_class_schema

Retrieves the schema definition for a specific class (collection)

get_cluster_nodes

Retrieves operational information about the Weaviate cluster nodes

get_full_schema

Retrieves the complete Weaviate schema (all collections)

get_instance_metadata

Retrieves metadata about the Weaviate instance

get_object_details

Retrieves a specific data object by its UUID

list_objects

Supports basic pagination via limit. Lists data objects within a specific class

search_near_vector

Provide a class name and a query vector as a JSON array of floats. Performs a nearest neighbor vector similarity search

Example Prompts for Weaviate in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Weaviate immediately.

"List all classes in my Weaviate schema."

"Search the 'Article' class for items similar to this vector: [0.12, -0.05, 0.88, ...]."

"What is the current health status of my Weaviate cluster nodes?"

Troubleshooting Weaviate MCP Server with Pydantic AI

Common issues when connecting Weaviate to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Weaviate + Pydantic AI FAQ

Common questions about integrating Weaviate 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 Weaviate MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.