How to Use the Float MCP in AutoGen

Q: How do I register Float MCP Server tools in AutoGen?

Install autogen-extmcp and use mcpservertools with your Vinkius HTTP URL. Pass the resulting tool list directly to the AssistantAgent constructor to give your agents access.

Q: Can AutoGen agents negotiate Float allocations?

Yes, agents can use listallocations to find overlaps and debate resolutions. Once they reach a consensus, the designated coordinator agent executes createallocation to update the schedule.

Q: How do agents handle conflicting Float tasks?

One agent flags the conflict using listallocations, while another checks alternative dates with listtimeoffs. They exchange messages in a conversation loop until they find a conflict-free slot.

Run multi-agent debates in AutoGen to negotiate and resolve Float project allocations automatically.

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Setup Float with AutoGen

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Consensus-driven Float scheduling with AutoGen

The Float MCP Server provides the raw data for AutoGen agents to debate and resolve complex resource conflicts by exposing `get_project` and `list_time_offs`. One AutoGen agent can represent the project budget using `get_project` while another agent defends team work-life balance using Float's `list_time_offs`. They negotiate using actual Float API data, coming to an AutoGen consensus before calling `create_allocation`. This prevents Float scheduling burnout by ensuring multiple AutoGen perspectives are weighed before any task is booked.

Automated department balancing

This setup uses `list_departments` and `list_people` to let separate AutoGen agents manage departmental workloads. An AutoGen design lead agent and a development lead agent negotiate over shared Float resources, checking availability in real-time. By using the AutoGen `mcp_server_tools` adapter, you expose these Float MCP Server tools to all agent conversations. The AutoGen agents dynamically request details using `get_person` to verify skills before finalizing Float team assignments.

Multi-agent productivity audits

Auditing project efficiency becomes a team effort when AutoGen agents analyze data from `get_logged_time` and `list_projects`. An AutoGen auditor agent flags discrepancies between scheduled Float allocations and actual hours, while a manager agent explains the variance. You integrate this Float workflow by passing the tool list to your AutoGen `AssistantAgent` constructor. The AutoGen agents communicate over stdio or HTTP transports to coordinate their analysis of your team's historical Float performance.

Setup guide

Set up Float 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 Float 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="Float_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install `autogen-ext[mcp]` and use `mcp_server_tools` with your Vinkius HTTP URL. Pass the resulting tool list directly to the `AssistantAgent` constructor to give your agents access.

Yes, agents can use `list_allocations` to find overlaps and debate resolutions. Once they reach a consensus, the designated coordinator agent executes `create_allocation` to update the schedule.

Yes, the adapter handles both stdio and Streamable HTTP transports. This allows your agents to query `list_user_accounts` and other endpoints securely across different environment setups.

One agent flags the conflict using `list_allocations`, while another checks alternative dates with `list_time_offs`. They exchange messages in a conversation loop until they find a conflict-free slot.

Vinkius runs the Float MCP Server in a zero-trust sandbox using ephemeral V8 Isolates. Your project plans, client names from `list_clients`, and team schedules are never cached or stored on the hosting platform.

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