How to Use the Moving Average Engine MCP in AutoGen

Q: How do I register the Moving Average Engine in AutoGen?

Install autogen-extmcp. Use mcpservertools with your endpoint URL, and pass the resulting tool list directly into your AssistantAgent constructor.

Give your AutoGen agents a deterministic math engine to settle financial debates.

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Connect Moving Average Engine MCP to AutoGen

Create your Vinkius account to connect Moving Average Engine 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 Moving Average Engine with AutoGen

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Settle Agent Debates with Hard Math

The `calculate_moving_average` tool computes exact Simple (SMA) and Exponential (EMA) moving averages for your AutoGen agents. When agents argue over market trends, they need a deterministic source of truth rather than probabilistic guesses. One agent proposes a trade based on momentum, and a risk-averse agent challenges the premise. They call this MCP Server with the raw data array, get the exact EMA, and base their consensus on actual mathematics.

Stop Hallucinated Numbers in AutoGen

Conversational frameworks often drift from reality when forced to do recursive floating-point math. By externalizing the calculation to a dedicated deterministic engine, you force the agents to rely on a calculator instead of their own token prediction. The schema strictly dictates that agents must provide a valid numerical array and period. If an agent tries to guess the trend, the system forces it to invoke the tool and wait for the exact result.

Build Complex Financial Workflows

Multi-agent systems thrive when quantitative analysts have actual calculators. You can assign this MCP tool specifically to a designated quant agent within your group chat. While the research agent reads news and the compliance agent checks rules, the quant agent uses the engine to calculate the 200-day SMA. The final decision emerges from a mix of qualitative analysis and guaranteed mathematical precision.

Setup guide

Set up Moving Average Engine 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 Moving Average Engine 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="Moving Average Engine_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Moving Average Engine_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

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Common questions about Moving Average Engine MCP in AutoGen

Install `autogen-ext[mcp]`. Use `mcp_server_tools` with your endpoint URL, and pass the resulting tool list directly into your `AssistantAgent` constructor.

You can share the tool across the entire group chat or restrict it to a specific quant agent. The framework handles the schema conversion automatically.

Native code interpreters add latency and require sandbox management. Calling a dedicated endpoint guarantees the exact formula executes instantly without risking syntax errors during the conversation.

When agents disagree on a trend direction, they query the tool. The deterministic output acts as an absolute fact that forces the conversational logic to converge on a single decision.

You pass raw arrays to the tool, and Vinkius executes the arithmetic in an isolated, ephemeral sandbox. The instance drops all memory of your data the millisecond the response returns to your client.

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