How to Use the GitScrum Knowledge MCP in AutoGen

Q: How do I register this MCP Server with AutoGen and GitScrum Knowledge?

Install autogen-extmcp and use mcpservertools with the server's HTTP URL. Pass the resulting tool list to your AssistantAgent constructor to give them access to all 28 tools.

Q: Can multiple AutoGen agents edit the same GitScrum Knowledge note?

Yes. Agents can read the note using listnotes, propose edits, and call updatenote to apply changes. They use noterevisions to track edits made by other agents in the conversation.

Q: How do AutoGen agents search for information in GitScrum Knowledge?

Agents execute globalsearch to query across all workspace resources. They can also use searchchannelmessages or searchwiki for more targeted queries.

Q: What happens if agents conflict while updating a wiki page?

The agents can inspect the history via wikirevisions to see the conflicting changes. If needed, they can call restorewikirevision to revert to a stable state before negotiating the next edit.

Let AutoGen agents debate, update, and search your GitScrum Knowledge base via this MCP Server.

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Create your Vinkius account to connect GitScrum Knowledge 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 GitScrum Knowledge with AutoGen

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Multi-Agent Debate on Notes

`create_note` acts as a shared scratchpad where multiple AutoGen agents record their individual findings and debate conclusions. One agent might write a proposal, while a reviewer agent edits it using `update_note` to reflect consensus. To keep track of the debate, agents use `note_revisions` to audit changes made by other agents. This ensures the final note represents a verified, collaborative decision rather than a single agent's hallucination.

Collaborative Wiki Generation via MCP Server

`create_wiki_page` allows a writer agent to draft technical documentation, while a separate editor agent refines the structure. The editor agent uses `list_wiki_pages` to find the correct parent page and nest the new document appropriately. If the editor finds errors, it calls `update_wiki_page` to correct them. The agents can coordinate via `list_discussions` to ensure their edits align with the broader project goals.

Threaded Discussion Moderation

`send_message` lets your AutoGen team post updates, status reports, or alerts directly to team channels. A monitoring agent can watch for issues and use `reply_to_message` to start a threaded discussion. Other agents join the thread using `thread_replies` to read the context and post their own analyses. This automates team communication, keeping human developers informed without manual intervention.

Setup guide

Set up GitScrum Knowledge 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 GitScrum Knowledge 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="GitScrum Knowledge_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install autogen-ext[mcp] and use mcp_server_tools with the server's HTTP URL. Pass the resulting tool list to your AssistantAgent constructor to give them access to all 28 tools.

Yes. Agents can read the note using list_notes, propose edits, and call update_note to apply changes. They use note_revisions to track edits made by other agents in the conversation.

Agents execute global_search to query across all workspace resources. They can also use search_channel_messages or search_wiki for more targeted queries.

The agents can inspect the history via wiki_revisions to see the conflicting changes. If needed, they can call restore_wiki_revision to revert to a stable state before negotiating the next edit.

No. The server runs in an isolated runtime where your notes, wiki revisions, and discussion logs are never stored or logged. Vinkius handles the authentication via an ephemeral token, so your agents never see your actual API keys.

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