How to Use the ZipRecruiter MCP in AutoGen

AutoGen: Get consensus-driven job decisions with your agent.

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Connect ZipRecruiter MCP to AutoGen

Create your Vinkius account to connect ZipRecruiter to AutoGen — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup ZipRecruiter with AutoGen

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Debating Search Criteria with AutoGen

You can set up a multi-agent system where one agent's sole job is to call the MCP tool. This first agent runs `ziprecruiter_search_jobs` based on initial parameters, providing a raw list of opportunities. A second agent then challenges those results, perhaps arguing that the salary range was too narrow or the keywords were too vague.

Consensus-Driven Job Selection via AutoGen

The beauty here is deliberation. Instead of just getting a list, you get convergence. Agents argue over which criteria are most important—is high salary more critical than location? They debate until they reach a consensus on the best search parameters. The MCP supports this by providing structured results for critique and refinement.

Structured Output for Multi-Agent Debate with AutoGen

The tool output is formatted to be easily consumed by multiple agents. The result of the job search isn't a final answer; it's evidence presented for debate. This ensures that every decision point is scrutinized. It forces your system to consider competing perspectives before committing.

Setup guide

Set up ZipRecruiter 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 ZipRecruiter 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="ZipRecruiter_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

The MCP provides the raw data (keywords, location, salary) that one agent calls. Other agents then debate and refine how those results should be interpreted or if new parameters need to be added.

You build a negotiation process. The system doesn't just search; it debates the best way to search, resulting in a highly validated set of job options that withstand internal critique.

Yes. You can program an agent to take initial results and then iteratively call the MCP with refined parameters, mimicking a human recruiter's refinement process until consensus is reached.

This MCP touches job listing details: keywords, specific geographic locations, and required salary ranges. These structured outputs are used as evidence during the multi-agent deliberation.

For complex decisions, yes. A single call gives you data; AutoGen forces your system to consider the implications of that data from multiple angles, giving you higher confidence in the final decision.

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