How to Use the GlassFrog MCP in AutoGen

Q: How do AutoGen agents handle connection issues with GlassFrog?

Your agents can run verifyapiconnection as a pre-flight check before executing complex tasks. If the check fails, the agent chain pauses and alerts the system administrator.

Let your AutoGen agents debate and coordinate complex GlassFrog governance updates via MCP before executing them.

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Connect GlassFrog MCP to AutoGen

Create your Vinkius account to connect GlassFrog 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 GlassFrog with AutoGen

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Perplexity

Manage tactical projects with an AutoGen consensus loop

The `create_new_project` tool inserts a new tactical project directly into your designated GlassFrog circle. In an AutoGen setup, a project-proposal agent drafts the initiative while a governance agent reviews it against current circle policies. Only after both agents agree on the scope does the framework execute the write call to your workspace. This collaborative vetting process prevents administrative bloat and keeps your project list clean.

Negotiate roles using MCP Server tools

The `list_role_assignments` tool retrieves the current list of members assigned to active roles. When a vacancy occurs, your AutoGen agents debate which member has the best capacity and skill set to fill it. A coordinator agent matches the vacant role's accountabilities with member profiles retrieved via `list_org_members`. This structured debate ensures that role proposals are thoroughly analyzed before any changes are finalized.

Analyze circle metrics using specialized agents

The `list_circle_metrics` tool pulls operational performance indicators directly from your active circles. One agent analyzes these metrics for negative trends while another agent searches for potential root causes. They synthesize their findings into a single report, highlighting which areas require immediate tactical attention. This automated debate saves your facilitators hours of manual analysis prior to monthly review meetings.

Setup guide

Set up GlassFrog 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 GlassFrog 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="GlassFrog_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Use the MCP tool adapter to convert the server's tools into the schema AutoGen expects. After that, pass this tool list directly to your AssistantAgent constructor during setup.

Yes, you can set up a human-in-the-loop proxy agent to approve any write actions. That way, no project is created in your workspace without explicit human sign-off.

Your agents can run `verify_api_connection` as a pre-flight check before executing complex tasks. If the check fails, the agent chain pauses and alerts the system administrator.

Yes, AutoGen can connect to multiple endpoints simultaneously. For instance, you can have one agent reading governance data while another pulls task data from a separate system.

Your organizational data, including member emails and role details, is protected by Vinkius's zero-trust MCP architecture. Every tool execution runs in an ephemeral V8 sandbox, preventing unauthorized access to your internal directory.

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