How to Use the Unleash (Feature Toggles) MCP in AutoGen

Q: How do I list all possible environments with Unleash (Feature Toggles) and AutoGen?

Call listenvironments. This provides a definitive list of all available environments. You can then have your agents debate which environment the feature should be tested in.

Q: What kind of data does Unleash (Feature Toggles) touch regarding users?

The server touches user details via listusers. An agent can pull this list and then have other agents debate if the feature should be rolled out based on user criteria.

Q: Can I find all available projects using Unleash (Feature Toggles) and AutoGen?

Use listprojects. This tool gives your agent a list of every project ID. The debate process can then determine the optimal deployment target from this comprehensive set.

Q: How do I register new SDK instances with Unleash (Feature Toggles) and AutoGen?

You use registerclient to initialize a backend connection. The resulting status of that registration can be passed into the agent pool, allowing agents to debate whether the connection is ready for action.

Q: If I need to know about feature segments, what tool should I use with Unleash (Feature Toggles) and AutoGen?

Use listsegments. This gathers all segment definitions. Multiple agents can then debate the precise conditions required for a feature based on these gathered segment rules.

Force consensus on feature logic with Unleash (Feature Toggles) using AutoGen's multi-agent debate framework.

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Setup Unleash (Feature Toggles) with AutoGen

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Evaluate Flags and Strategies via `get_client_features`

The `get_client_features` tool provides all feature flags and strategies for evaluation. When feeding this into AutoGen, one agent can pull the data, and another agent can debate whether a specific flag is enabled for production. The multi-agent system forces deliberation on complex rules before reaching a final decision.

Check Project Context for Feature Toggles

The `list_projects` tool lets your agents retrieve every configured project ID. This list serves as the initial context in an AutoGen debate: 'Should we launch this feature on Project A or B?' The consensus mechanism makes sure all angles—like segment restrictions—are discussed.

Manage User and Segment Context

Need to know who's using the tool? Use `list_users` for user lists, or `list_segments` for group definitions. Your agents use these tools as inputs that multiple personas debate over. For example, one agent might flag a feature because it only applies to 'Premium Users,' and another might argue against it based on segment restrictions.

Setup guide

Set up Unleash (Feature Toggles) 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 Unleash (Feature Toggles) 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="Unleash (Feature Toggles)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Unleash (Feature Toggles) 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="Unleash (Feature Toggles)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Unleash (Feature Toggles) data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Unleash (Feature Toggles) MCP in AutoGen

Call `list_environments`. This provides a definitive list of all available environments. You can then have your agents debate which environment the feature should be tested in.

The server touches user details via `list_users`. An agent can pull this list and then have other agents debate if the feature should be rolled out based on user criteria.

Use `list_projects`. This tool gives your agent a list of every project ID. The debate process can then determine the optimal deployment target from this comprehensive set.

You use `register_client` to initialize a backend connection. The resulting status of that registration can be passed into the agent pool, allowing agents to debate whether the connection is ready for action.

Use `list_segments`. This gathers all segment definitions. Multiple agents can then debate the precise conditions required for a feature based on these gathered segment rules.

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