How to Use the Verba MCP in LangChain

Q: How do I list documents using Verba and LangChain?

You call listknowledgedocuments. This tool pulls every document indexed in the Verba knowledge base. Your agent gets a full manifest of available sources.

Q: Can I delete documents using Verba and LangChain?

Yes, you use deleteknowledgedocument. Be careful—this action is irreversible. The tool permanently removes a document from the knowledge base.

Q: Does Verba support updating metadata with LangChain?

The addknowledgedocument tool accepts optional metadata JSON when ingesting content. This means you can programmatically update or add context to existing documents.

Q: Which tools are available on the Verba MCP Server for LangChain?

Verba offers six core tools, including performragquery, addknowledgedocument, and listknowledgedocuments. These let your agent manage and query all aspects of the knowledge base.

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Connect Verba MCP to LangChain

Create your Vinkius account to connect Verba to LangChain 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 Verba with LangChain

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ChatGPT

Claude

Perplexity

Run RAG queries against your knowledge base.

The `perform_rag_query` tool executes a Retrieval Augmented Generation query. Your agent searches the Verba knowledge base, retrieves semantic answers, and provides citations for everything it says. This lets you connect your AI client directly to your Verba RAG platform. You manage your Weaviate knowledge base right within your LangChain chain.

Ingest new documents into the system.

Use `add_knowledge_document` when you need to add a source file. This tool takes document content and optional metadata JSON, getting it indexed into the Verba knowledge base. It's how you feed your agent fresh information. You can build pipelines that automatically ingest documents before running complex reasoning chains.

Examine system setup details.

Need to know what configuration is active? Call `get_system_config`. This tool pulls the current Verba system settings, letting your agent read the operational parameters. This gives you visibility into the MCP Server's state. You can build steps that check necessary prerequisites before running a main workflow.

Setup guide

Set up Verba MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Verba tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "verba-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Verba transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Verba MCP in LangChain

You call `list_knowledge_documents`. This tool pulls every document indexed in the Verba knowledge base. Your agent gets a full manifest of available sources.

Verba exposes both structured metadata and raw content through tools like `get_document_details` and `list_knowledge_documents`. Your agent can read the full details of a specific document.

Yes, you use `delete_knowledge_document`. Be careful—this action is irreversible. The tool permanently removes a document from the knowledge base.

The `add_knowledge_document` tool accepts optional metadata JSON when ingesting content. This means you can programmatically update or add context to existing documents.

Verba offers six core tools, including `perform_rag_query`, `add_knowledge_document`, and `list_knowledge_documents`. These let your agent manage and query all aspects of the knowledge base.

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