How to Use the GitScrum ClientFlow MCP in AutoGen

Let autonomous AutoGen agents debate budgets, log hours, and draft invoices through GitScrum ClientFlow.

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Connect GitScrum ClientFlow MCP to AutoGen

Create your Vinkius account to connect GitScrum ClientFlow 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 ClientFlow with AutoGen

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Let AutoGen agents debate and verify project budgets

This MCP Server gives your AutoGen agents direct access to `list_time_entries` and `project_budget` for collaborative budget reviews. You can set up a multi-agent conversation where a PM agent and a finance agent review agency health. The PM agent pulls live tracking data while the finance agent checks the limits. They discuss whether the current pace is sustainable, and if they agree the project is burning money too fast, they can automatically coordinate with another agent to check `get_client` details for key contact info.

Multi-agent invoice review and creation

This MCP Server enables a multi-agent billing workflow in AutoGen by exposing `create_invoice` and `get_proposal` to your agents. One agent drafts an invoice and another checks it for errors, using `get_proposal` to compile the line items and prepare the draft. Before the tool is executed, an auditor agent reviews the proposed JSON payload against past bills retrieved via `list_invoices`. They negotiate the final structure in the chat, ensuring you don't send incorrect totals to clients.

Automated time logging verification

This MCP Server lets your AutoGen agents use `log_time` to automatically record hours against active client tasks. An agent can monitor developer activity and log hours, while a supervisor agent checks the entries against active client profiles. By querying `clientflow_dashboard`, the supervisor agent ensures logged time aligns with the overall project phase. If a discrepancy is found, the agents debate the issue in the conversation thread before finalizing the logs.

Setup guide

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

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

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

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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 ClientFlow MCP in AutoGen

You register the tools with your agent group. A PM agent might call `list_time_entries` and share the logs in the group chat, allowing a finance agent to use that data to calculate the next invoice.

Yes, you can configure your agent to require human approval. The agent drafts the payload for `create_invoice`, presents it in the chat, and only executes the tool once you give the green light.

All agents in the conversation share the same tool context. If one agent retrieves client details using `get_client`, those details are visible in the chat history, so other agents can reference them without repeating the API call.

It uses standard HTTP transport managed securely by Vinkius. This MCP Server allows your agents to communicate with the API reliably, converting schemas automatically behind the scenes.

This MCP Server accesses your data on-demand through an ephemeral sandbox. Vinkius handles authentication using a single token, ensuring your client profiles, proposals, and invoices are never exposed to external actors.

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