How to Use the Treblle MCP in AutoGen

Q: How do I use Treblle to inform AutoGen debates?

You integrate ingestapidata into your agent workflow. This makes real-time API data available, allowing agents to debate and challenge conclusions based on observed network traffic.

Q: Can the MCP Server handle sensitive data for AutoGen?

Yeah, it can. The ingestapidata tool automatically masks fields like passwords and CCs before they get processed by your multi-agent conversation. It keeps everything secure.

Drive consensus decisions with AutoGen. Debate API findings using Treblle's MCP Server tools.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Treblle MCP to AutoGen

Create your Vinkius account to connect Treblle 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.

GDPR Free for Subscribers

Setup Treblle with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Debating based on real API inputs

The `ingest_api_data` tool provides a shared, factual source of truth: your API traffic logs. Multiple agents can debate conclusions using data captured by the MCP Server. Instead of arguing from theory, your security agent and performance agent both challenge each other's positions based on the specific requests/responses documented via `ingest_api_data`.

Consensus over conflicting reports

AutoGen excels when the answer isn't clear. By passing API data through `ingest_api_data`, you give agents concrete inputs to negotiate around. They must converge on a decision that makes sense given the actual observed network traffic. This capability turns debate into actionable system design, requiring multiple perspectives to reconcile conflicting observations from the MCP Server.

Automated data observation

You don't need manual logging. The `ingest_api_data` tool automatically monitors and documents API traffic in real-time. This structured output feeds directly into your multi-agent conversation. It acts as a shared blackboard, ensuring every agent—whether it's checking for risk or optimizing speed—is working from the exact same dataset of requests and responses.

Setup guide

Set up Treblle 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 Treblle 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="Treblle_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Treblle_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Treblle. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Treblle now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Treblle MCP in AutoGen

You integrate `ingest_api_data` into your agent workflow. This makes real-time API data available, allowing agents to debate and challenge conclusions based on observed network traffic.

Yeah, it can. The `ingest_api_data` tool automatically masks fields like passwords and CCs before they get processed by your multi-agent conversation. It keeps everything secure.

Absolutely. By grounding the debate in objective API data, the MCP Server forces consensus on outcomes that align with actual observed request and response patterns.

The server captures real-time API request and response data. The `ingest_api_data` tool documents this traffic, giving your agents a rich source of operational context.

Treblle touches sensitive API request and response data. The `ingest_api_data` tool ensures that any confidential information, such as SSNs, is automatically masked before it participates in agent deliberation.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript