How to Use the Cognita (RAG Framework) MCP in AutoGen

Q: How do AutoGen agents resolve conflicting data from Cognita (RAG Framework)?

Your agents use their native debate protocols. If searchchunks returns conflicting presets, the critic agent requests a new run of ragquery to reconcile the differences.

Q: What package do I need to connect AutoGen to this server?

You need to install autogen-extmcp via pip. This extension provides the McpToolAdapter which translates the tools into schemas AutoGen understands.

Run multi-agent debates in AutoGen using Cognita (RAG Framework) tools to reach consensus.

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Connect Cognita (RAG Framework) MCP to AutoGen

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Setup Cognita (RAG Framework) with AutoGen

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Debate RAG queries using `rag_query` in AutoGen

The `rag_query` tool identifies precise active arrays spanning rented transformation vectors for your AutoGen agents to analyze. A retriever agent pulls the data, while a critic agent challenges the vector accuracy before finalizing the answer. This collaborative loop ensures that RAG outputs are thoroughly verified by multiple perspectives. Your AutoGen setup won't settle for a single raw query result without checking it against validation rules.

Audit cloud logging with `get_collection`

The `get_collection` tool retrieves explicit cloud logging tracing explicit payload IDs for your debugging agents. When an execution fails, a specialized diagnostic agent calls this tool to inspect the traces. The diagnostic agent then debates with your execution agent to pinpoint exactly which payload caused the issue. This makes error resolution completely hands-off.

Coordinate ingestion via `ingest_data`

The `ingest_data` tool provisions a highly-available JSON payload generating new resource directories after consensus is reached. This MCP Server workflow has one agent draft the payload, a security agent validate the schema, and a third agent execute the ingestion. This multi-agent gatekeeper pattern stops bad data from corrupting your RAG framework. You get clean, validated resource directories every single time.

Setup guide

Set up Cognita (RAG Framework) 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 Cognita (RAG Framework) 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="Cognita (RAG Framework)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Cognita (RAG Framework) 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="Cognita (RAG Framework)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

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Common questions about Cognita (RAG Framework) MCP in AutoGen

You register the tools using the MCP adapter and pass them to your AssistantAgent constructors. Multiple AutoGen agents can call `rag_query` or `list_collections` during their conversation to verify facts.

Yes. You configure StreamableHttpServerParams pointing to the Vinkius URL inside your AutoGen script. This MCP setup handles all schema conversions automatically via the adapter.

Your agents use their native debate protocols. If `search_chunks` returns conflicting presets, the critic agent requests a new run of `rag_query` to reconcile the differences.

You need to install autogen-ext[mcp] via pip. This extension provides the McpToolAdapter which translates the tools into schemas AutoGen understands.

Only your local AutoGen agents can access them. Vinkius runs this MCP server in a secure, ephemeral V8 isolate that handles authentication seamlessly, meaning your cloud logging stays completely private.

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