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 LangChain 6 tools — connect in under 2 minutes

Name: Verba
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Verba through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "verba": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Verba, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

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.

LangChain's ecosystem of 500+ components combines seamlessly with Verba through native MCP adapters. Connect 6 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Verba MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 6 tools from Verba via MCP

Why Use LangChain with the Verba MCP Server

LangChain provides unique advantages when paired with Verba through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Verba MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Verba queries for multi-turn workflows

Verba + LangChain Use Cases

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

RAG with live data: combine Verba tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Verba, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Verba tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Verba tool call, measure latency, and optimize your agent's performance

Verba MCP Tools for LangChain (6)

These 6 tools become available when you connect Verba to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Verba + LangChain FAQ

Common questions about integrating Verba MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.