How to Use the Yelp Fusion MCP in AutoGen

Q: How does AutoGen help with Yelp Fusion business discovery?

You assign different roles. One agent can run searchbusinesses, and a second agent, acting as the 'vet,' must then call getbusinessdetails to validate the results before giving you a final answer.

Q: If I need to find jobs, does AutoGen handle Yelp Fusion job types?

Yes. You can have agents debate the best query for getjobs. One might suggest 'plumbing,' and another suggests broadening it to 'home repair' based on initial review data.

Q: What if I need a list of all categories? Can AutoGen manage that?

You can assign an agent the sole task of running getcategories. This single, focused action provides the full taxonomy of business types available in Yelp Fusion.

Q: What data type is stored when using Yelp Fusion with AutoGen?

The server touches business details. When multiple agents interact with this tool, they generate a consensus on specific structured pieces of information. This final decision structure is the most valuable output.

Get consensus on Yelp Fusion data using AutoGen agents.

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Connect Yelp Fusion MCP to AutoGen

Create your Vinkius account to connect Yelp Fusion 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 Yelp Fusion with AutoGen

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Perplexity

Debating Business Viability

Need to decide if a business is worth it? Set up multiple agents. One agent uses `get_business_reviews` for sentiment, while another runs `get_business_insights` for financial trends. The consensus engine forces them to negotiate the final recommendation based on conflicting data points.

Cross-Checking Services and Events

Imagine two agents: one checks if a business offers specific services via `get_service_offerings`, and another uses `search_events` to check for local activities. They debate the best time to visit. The final output is not just a list, but a justified plan based on their collective discussion.

Automating Complex Search Logic

Use one agent to run `autocomplete` for keywords and another to refine that search using `search_businesses`. They argue over the best parameters. The system converges on a highly specific, validated set of results you didn't know how to query manually.

Setup guide

Set up Yelp Fusion 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 Yelp Fusion 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="Yelp Fusion_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You assign different roles. One agent can run `search_businesses`, and a second agent, acting as the 'vet,' must then call `get_business_details` to validate the results before giving you a final answer.

Definitely. You can set up agents that debate which local events are best by running both `search_events` and then filtering those options using the details from `get_event_details`.

Yes. You can have agents debate the best query for `get_jobs`. One might suggest 'plumbing,' and another suggests broadening it to 'home repair' based on initial review data.

You can assign an agent the sole task of running `get_categories`. This single, focused action provides the full taxonomy of business types available in Yelp Fusion.

The server touches `business details`. When multiple agents interact with this tool, they generate a consensus on specific structured pieces of information. This final decision structure is the most valuable output.

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