How to Use the Beamer MCP in AutoGen

Q: How do I add Beamer tools to my AutoGen agents?

Install autogen-extmcp. Use mcpservertools with StreamableHttpServerParams pointing to your Vinkius endpoint, then pass the resulting list to your AssistantAgent.

Q: How do multiple agents handle Beamer user feedback?

One agent acts as the researcher, calling listfeedback and getfeedbackdetails. It passes the raw data to an analyst agent, which interprets the sentiment and suggests product improvements.

Give your AutoGen multi-agent squads the ability to debate and publish Beamer updates together.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Beamer MCP to AutoGen

Create your Vinkius account to connect Beamer 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 Beamer with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Beamer MCP Server drafting

Writing Beamer release notes requires input from different departments. You can assign a developer agent to summarize code commits while a marketing agent reviews the text for tone. They debate the messaging before touching the integration. Once the agents reach a consensus, the publishing agent takes over. It executes `create_post` to push the agreed-upon draft to your feed. If the marketing agent spots an error post-publish, it argues for a revision and triggers `update_post`.

Analyze feedback through consensus

Interpreting Beamer customer reactions is rarely straightforward. A support agent runs `list_feedback` to gather recent comments, while a product agent pulls the corresponding release details using `get_post`. The two agents discuss the correlation between the update and the user sentiment. If they need more granular data, one will call `get_feedback_details` through the MCP connection to investigate specific complaints before finalizing their joint report.

Audit engagement metrics autonomously

Evaluating Beamer feature success works best with competing perspectives. A data agent might pull raw numbers using `get_analytics`, focusing strictly on view counts and click-through rates. Meanwhile, a community agent checks `list_notifications` to gauge organic interaction. They compare their findings, negotiating the actual impact of the release without requiring human intervention.

Setup guide

Set up Beamer 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 Beamer 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="Beamer_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Beamer 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="Beamer_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Beamer 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 Beamer. 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 Beamer 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 Beamer MCP in AutoGen

Install autogen-ext[mcp]. Use mcp_server_tools with StreamableHttpServerParams pointing to your Vinkius endpoint, then pass the resulting list to your AssistantAgent.

They are designed for exactly that. A drafting agent writes the content while a reviewer agent checks it. After reaching an agreement, one agent fires `create_post` to publish the update.

One agent acts as the researcher, calling `list_feedback` and `get_feedback_details`. It passes the raw data to an analyst agent, which interprets the sentiment and suggests product improvements.

It does. The McpToolAdapter automatically converts the API schemas into formats your agents understand. You just pass the tools, and AutoGen handles the parameter mapping.

Vinkius isolates every session. When your agents pull user comments and email addresses from Beamer, that sensitive information travels through a stateless sandbox that destroys itself after the execution completes.

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