Can I query my local Verba instance directly through Cursor?

Yes! Once you configure `VERBA_API_URL` to point to `http://localhost:8000` (or your host port), you can prompt your AI assistant to execute rigorous `perform_rag_query` instructions without ever breaking your developer focus.

How do I insert fresh text data into Verba completely using conversational chat?

Provide the agent with your desired context directly. For example: `Add this chunk of markdown as a new document to Verba: '# Title Content...'`. The agent leverages `addDocumentTool`, serializes the payload, and commits it into Verba's vector store immutably.

Are the query answers backed by citations from its embedded documents?

Absolutely. That's the primary benefit of the integration. When you run `perform_rag_query`, Verba utilizes Weaviate's hybrid search mechanics. The output explicitly includes natural language synthesis backed by the unique document IDs and snippet texts it referenced.

Verba MCP Server for Pydantic AI 6 tools — connect in under 2 minutes

Name: Verba
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Verba 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 Verba "
            "(6 tools)."
        ),
    )

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

Intertwine the open-source Verba (by Weaviate) ecosystem natively into your conversational AI IDE. Execute powerful Retrieval-Augmented Generation processes and manage your localized knowledge bases simply by chatting.

Pydantic AI validates every Verba tool response against typed schemas, catching data inconsistencies at build time. Connect 6 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

Augmented Queries — Cast a question to your agent and have it retrieve fully synthesized answers from the Verba engine completely backed up by exact document citations.
Knowledge Management — Insert new context text, list all ingested documents, retrieve the deeply embedded raw data of any ID, or remove dead knowledge dynamically without Web UIs.
Health Checks — Request system configurations directly via chat to ensure your local LLM connections, embedding models, and cluster health are firing effectively.

The Verba MCP Server exposes 6 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 Verba to Pydantic AI via MCP

Follow these steps to integrate the Verba 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 6 tools from Verba with type-safe schemas

Why Use Pydantic AI with the Verba MCP Server

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

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

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

Verba + Pydantic AI Use Cases

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

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

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

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

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

Verba MCP Tools for Pydantic AI (6)

These 6 tools become available when you connect Verba to Pydantic AI via MCP:

add_knowledge_document

Provide the document content and optional metadata JSON. Ingests a new document into the Verba knowledge base

delete_knowledge_document

This action is irreversible. Permanently removes a document from the knowledge base

get_document_details

Retrieves the full content and metadata of a specific document

get_system_config

Retrieves the current Verba system configuration

list_knowledge_documents

Lists all documents indexed in the Verba knowledge base

perform_rag_query

Returns summarized answers with citations. Executes a RAG (Retrieval Augmented Generation) query against the Verba knowledge base

Example Prompts for Verba in Pydantic AI

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

"Check Verba's configuration to see which embedding model it is currently using."

"Perform a RAG query asking: 'What are our key deployment steps based on the infrastructure guide?'"

"List all documents and output the unique ID of the 'Employee Code of Conduct' file."

Troubleshooting Verba MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Verba + Pydantic AI FAQ

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