How to Use the Verba MCP in AutoGen

Q: How do I list all indexed documents with Verba and AutoGen?

You use listknowledgedocuments. This tool provides the full index of available files in the Verba knowledge base. The agents can then debate which document is most relevant.

Q: Can I remove documents using Verba and AutoGen?

Yes, deleteknowledgedocument permanently removes a file from the knowledge base. If an agent decides data is obsolete, it can initiate this action.

Q: Does Verba support detailed document checks with AutoGen?

The getdocumentdetails tool retrieves full content and metadata for a specific file. This gives agents all the necessary detail to debate its veracity.

Drive consensus decisions using Verba and AutoGen's multi-agent framework.

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Create your Vinkius account to connect Verba to AutoGen 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 AutoGen

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Perplexity

Execute knowledge queries through debate.

When an answer isn't clear, your agents can use `perform_rag_query`. Multiple agents discuss the results from Verba's RAG query, challenging assumptions and converging on a single decision. This is perfect for complex systems. The MCP Server provides the raw data that competing perspectives must negotiate over.

Feed source material into agent debate.

Agents use `add_knowledge_document` to ingest new context into the Verba knowledge base. This means a 'research' agent can feed documents, and then the whole group debates based on that new info. The system treats this knowledge as verifiable fact during its deliberation process.

Check operational limits before action.

Before making a change, have an 'auditor' agent call `get_system_config`. This tool retrieves the current Verba system settings, allowing agents to debate whether the environment supports the proposed action. It adds a crucial layer of consensus-driven validation.

Setup guide

Set up Verba MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Verba tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Verba_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Verba data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Verba_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Verba data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Verba. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Verba MCP in AutoGen

You use `list_knowledge_documents`. This tool provides the full index of available files in the Verba knowledge base. The agents can then debate which document is most relevant.

Verba gives access to document content, metadata JSON, and system configuration details. These structured inputs allow your multi-agent group to build robust consensus decisions.

Yes, `delete_knowledge_document` permanently removes a file from the knowledge base. If an agent decides data is obsolete, it can initiate this action.

The `get_document_details` tool retrieves full content and metadata for a specific file. This gives agents all the necessary detail to debate its veracity.

The core data types are document contents and associated metadata JSON, which are critical inputs for the agent group's deliberation process.

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