How to Use the Grain MCP in AutoGen

Q: How do I configure the Grain MCP Server for AutoGen?

Install autogen-extmcp and use mcpservertools with the Vinkius transport URL. Pass the resulting tool list directly to your AssistantAgent constructor for automatic schema conversion.

Q: Can AutoGen agents collaborate to analyze a Grain recording?

Yes, one agent can retrieve the raw transcript using gettranscript while another pulls key highlights with listhighlights. They can then discuss the meeting contents to form a joint summary.

Q: How do AutoGen agents handle video uploads to Grain?

An agent can trigger uploadvideo with an external URL when instructed. A second agent can poll getrecording until the video is processed, then notify the rest of the group.

Q: How is my personal profile data protected in AutoGen?

Your user profile details and meeting data accessed via getcurrentuser are protected by Vinkius's zero-trust architecture. The MCP Server executes in an ephemeral sandbox, meaning no credentials or transcripts are stored on intermediate servers.

Let your AutoGen agents debate meeting transcripts and coordinate action items using this MCP Server.

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Create your Vinkius account to connect Grain 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 Grain with AutoGen

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Multi-agent consensus on Grain meeting summaries

This MCP Server lets you build a team of AutoGen agents that collaborate to analyze your meetings. One agent can call `get_transcript` to pull the raw text, while a second agent calls `get_insights` to extract key takeaways. They can then debate whether the automated insights accurately reflect the actual conversation. This collaborative approach ensures higher quality summaries. By discussing discrepancies before final logging, your agents can flag missed points or misinterpretations before they reach your CRM.

Coordinate action items using AutoGen agents

Assigning tasks no longer requires manual sorting. A project management agent can call `get_action_items` to retrieve tasks from a recording. It can then negotiate with a resource allocation agent to match those tasks against workspace members retrieved via `list_workspace_members`. This interaction ensures that tasks are assigned based on actual availability and roles. The agents can even use `list_tags` to categorize the tasks before finalizing the assignments.

AutoGen multi-agent search and verification

When searching through historical data, one agent can run `search_recordings` to find relevant meetings. A second auditor agent can verify the results by calling `get_recording` to ensure the context matches the search intent. This prevents agents from acting on irrelevant information. If a clip needs to be shared, a communication agent can use `list_shared_clips` to check if it has already been distributed. This multi-agent verification loop keeps your team aligned and prevents duplicate work.

Setup guide

Set up Grain 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 Grain 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="Grain_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install autogen-ext[mcp] and use mcp_server_tools with the Vinkius transport URL. Pass the resulting tool list directly to your AssistantAgent constructor for automatic schema conversion.

Yes, one agent can retrieve the raw transcript using `get_transcript` while another pulls key highlights with `list_highlights`. They can then discuss the meeting contents to form a joint summary.

An agent can trigger `upload_video` with an external URL when instructed. A second agent can poll `get_recording` until the video is processed, then notify the rest of the group.

Yes, you can filter the tool list returned by the server before passing it to the agent constructor. This allows you to restrict certain agents to read-only tools like `list_recordings` while blocking upload capabilities.

Your user profile details and meeting data accessed via `get_current_user` are protected by Vinkius's zero-trust architecture. The MCP Server executes in an ephemeral sandbox, meaning no credentials or transcripts are stored on intermediate servers.

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