How to Use the Glama MCP in AutoGen

Q: How do AutoGen agents use Glama tools?

They call glamalistmcpservers to find tools and then include them in their debate loop. You pass the tools list to your AssistantAgent constructor.

Q: Can AutoGen agents share a Glama gateway?

They do. By using glamarungatewaychat, all agents in your AutoGen conversation route their requests through the same unified proxy.

Q: Is Glama telemetry useful for AutoGen debates?

It is. You use glamasendtelemetry to record how many steps your AutoGen agents took to reach a conclusion.

Let your AutoGen agents debate and negotiate using tools discovered via the Glama MCP Server.

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Connect Glama MCP to AutoGen

Create your Vinkius account to connect Glama 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 Glama with AutoGen

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Negotiated discovery in AutoGen

Enable your agents to find and agree on tools. They trigger `glama_list_mcp_servers` and discuss which ones fit their current task. This allows your AutoGen system to dynamically expand its capabilities. Agents negotiate based on the directory results they retrieve.

Proxy chat routing in AutoGen

Route debates through a single gateway. Agents use `glama_run_gateway_chat` to ensure all participants use consistent model configurations. It keeps the conversation focused. Your agents don't waste time on configuration mismatches because the proxy enforces the model parameters.

Metric reporting for AutoGen

Track how your agents reach consensus. After a debate, call `glama_send_telemetry` to log the performance of your multi-agent system. This provides a clear view of how different agents interact with the tools. You see exactly which tool calls drive the final decision.

Setup guide

Set up Glama 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 Glama 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="Glama_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Glama MCP in AutoGen

They call `glama_list_mcp_servers` to find tools and then include them in their debate loop. You pass the tools list to your AssistantAgent constructor.

They do. By using `glama_run_gateway_chat`, all agents in your AutoGen conversation route their requests through the same unified proxy.

It is. You use `glama_send_telemetry` to record how many steps your AutoGen agents took to reach a conclusion.

It does. The server handles concurrent tool requests, allowing multiple AutoGen agents to query the directory or proxy chat at once.

Access is restricted to your specific account token. The server logs only the metadata of your tool executions, keeping your agent debate logs private.

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