How to Use the Verba MCP in LlamaIndex

Q: How does Verba handle document listing with LlamaIndex?

You call listknowledgedocuments. This tool pulls every indexed file name and metadata entry from the Verba knowledge base. It gives your agent a full inventory.

Q: Is it safe to delete documents using Verba and LlamaIndex?

The deleteknowledgedocument tool permanently removes files. Because LlamaIndex is building a knowledge index, make sure you've saved what you need before running this.

Q: Which tools should I use for advanced queries with Verba and LlamaIndex?

You'll primarily use performragquery for semantic search. For basic listing, listknowledgedocuments works great too.

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Connect Verba MCP to LlamaIndex

Create your Vinkius account to connect Verba to LlamaIndex 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 LlamaIndex

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Query knowledge using semantic search.

The `perform_rag_query` tool runs a Retrieval Augmented Generation query. This process lets your agent search the Verba knowledge base, retrieve answers based on meaning, and ground them with citations. Since LlamaIndex indexes results into vector stores, every MCP Server output becomes part of a unified, searchable index. You're not getting hallucinations.

Manage document ingestion for RAG.

Start by running `add_knowledge_document`. This tool ingests new documents into the Verba knowledge base, letting your agent add fresh data sources. Developers build applications where live API data and these newly indexed documents are combined. The output of this MCP Server directly feeds into the vector store.

Retrieve document metadata easily.

To check a specific file, use `get_document_details`. This tool retrieves both the full content and all associated metadata for one document. It's great for building workflows that need to validate source material. You can combine this with other tools like `list_knowledge_documents`.

Setup guide

Set up Verba MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Verba MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Verba tools.",
)
response = await agent.run("List recent Verba data")

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Common questions about Verba MCP in LlamaIndex

You call `list_knowledge_documents`. This tool pulls every indexed file name and metadata entry from the Verba knowledge base. It gives your agent a full inventory.

Verba provides document content, optional metadata JSON, and system configuration details via `get_system_config`. LlamaIndex indexes all this rich API data for you.

The `delete_knowledge_document` tool permanently removes files. Because LlamaIndex is building a knowledge index, make sure you've saved what you need before running this.

Yes. The MCP Server output becomes part of the searchable index. This means your agent can query historical API data or previous configuration results, not just live documents.

You'll primarily use `perform_rag_query` for semantic search. For basic listing, `list_knowledge_documents` works great too.

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