How to Use the Gamma (AI Presentations) MCP in AutoGen

Q: How do I give AutoGen agents access to Gamma (AI Presentations)?

Use mcpservertools with StreamableHttpServerParams. The framework automatically maps the API schema to functions your agents can call.

Let AutoGen agents debate, draft, and refine your presentations through this MCP Server.

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Connect Gamma (AI Presentations) MCP to AutoGen

Create your Vinkius account to connect Gamma (AI Presentations) 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 Gamma (AI Presentations) with AutoGen

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Multi-Agent Deck Drafting

This MCP Server allows your conversational agents to programmatically draft and edit presentations. You set up a researcher agent to gather facts and a designer agent to structure the slides. They negotiate the content before calling `create_gamma`. The researcher pushes for more detail, while the designer argues for brevity. Once they reach consensus, the final payload hits the API to generate the actual document.

Collaborative AutoGen MCP Server Edits

First drafts are just starting points. A reviewer agent can pull the newly created file using `get_gamma` and critique the visual structure. If the reviewer flags an issue, the formatting agent steps in. It executes `update_gamma` to fix the layout, creating a closed-loop editing system without human intervention.

Workspace Consensus and Cleanup

Agents need to know what already exists before they start building. A context agent runs `list_gammas` to audit the current workspace inventory before drafting begins. Redundant drafts get flagged during the debate phase. The manager agent then makes the final call to execute `delete_gamma`, keeping your environment organized while the swarm operates autonomously.

Setup guide

Set up Gamma (AI Presentations) 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 Gamma (AI Presentations) 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="Gamma (AI Presentations)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Gamma (AI Presentations) 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="Gamma (AI Presentations)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Gamma (AI Presentations) 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 Gamma. 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 Gamma (AI Presentations) MCP in AutoGen

Use mcp_server_tools with StreamableHttpServerParams. The framework automatically maps the API schema to functions your agents can call.

Yes. They share the tool context. One agent can draft the file while another runs `update_gamma` to refine specific sections.

You can filter the tool list before passing it to the AssistantAgent. Simply exclude the deletion endpoint from the array.

The framework feeds HTTP errors back into the conversation thread. The active agent reads the failure reason and attempts a different parameter configuration.

Nobody. Vinkius isolates webpage drafts and slide structures inside transient containers. We broker the API request, return the response to your client, and immediately burn the execution state.

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