How to Use the CheapShark MCP in AutoGen

Q: Can multiple AutoGen agents use CheapShark simultaneously?

Yes, different agents can call getgame and getstores during their conversation. They share the outputs to debate which store offers the best deal.

Q: How does AutoGen resolve store names using CheapShark?

One agent fetches the deals, and another uses getstores to map the store IDs. They collaborate to present a clean, readable list of store names to the user.

Q: Is my personal email safe when AutoGen sets alerts?

Your email address, used in managealert to configure price drops, is sent directly to the destination API. Vinkius runs the execution in an ephemeral sandbox, ensuring your contact info is never logged or exposed.

Run multi-agent debates in AutoGen to find and verify the best game deals.

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Connect CheapShark MCP to AutoGen

Create your Vinkius account to connect CheapShark 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 CheapShark with AutoGen

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Debate Deal Quality with AutoGen Agents

`get_deals` pulls current sales, but not every discount is actually a good value. In AutoGen, one agent can fetch the deals while a second agent analyzes the historical pricing of those specific games. This collaborative review filters out weak discounts, leaving only genuine bargains. You get a curated list of recommendations backed by multi-agent consensus.

Verify Game Specs Across Storefronts

`search_games` looks up titles across multiple platforms to find the correct Steam ID. A dedicated search agent handles this task, passing the verified IDs to a buyer agent. The buyer agent then runs `get_game` to check if the current price is worth the purchase. This division of labor prevents errors and ensures your agents act on correct game metadata.

Coordinate Price Alerts via MCP Server

`manage_alert` registers email notifications when prices drop below a threshold. Under this MCP server setup, a budget agent can negotiate with a shopping agent to decide the ideal target price before setting the alert. Once they agree on a realistic target, the system triggers the registration tool. This automated negotiation keeps your price alerts grounded in actual market trends.

Setup guide

Set up CheapShark 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 CheapShark 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="CheapShark_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You use the autogen-ext package to adapt and wrap the MCP tools for your AssistantAgent. This exposes `get_deals` and `search_games` directly to your conversational agents.

Yes, different agents can call `get_game` and `get_stores` during their conversation. They share the outputs to debate which store offers the best deal.

One agent fetches the deals, and another uses `get_stores` to map the store IDs. They collaborate to present a clean, readable list of store names to the user.

The server handles concurrent requests smoothly when your AutoGen agents run parallel checks. You can have one agent searching games while another manages active alerts.

Your email address, used in `manage_alert` to configure price drops, is sent directly to the destination API. Vinkius runs the execution in an ephemeral sandbox, ensuring your contact info is never logged or exposed.

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