How to Use the Workable Alternative MCP in AutoGen

Q: How does AutoGen use Workable Alternative to discuss candidate status?

The agents call listcandidates() first. They then debate which candidates should be prioritized, using specific details from getcandidate() to challenge or support each other's conclusions.

Q: Can AutoGen analyze job openings with Workable Alternative?

Yes. The agents use listjobs() and getjob() in a debate format. They discuss the scope, required skills, and priority of different job postings.

Q: If I need to check available recruiters, how does AutoGen use Workable Alternative?

The agents access the roster using listrecruiters(). They can then debate which specific recruiter is best equipped to handle a newly identified job opening.

Q: Does AutoGen touch candidate profile data from Workable Alternative?

Absolutely. It uses getcandidate() details as primary evidence points during the agentic debate, ensuring decisions are grounded in real-time applicant information.

Multi-Agent Debate on Recruitment: Workable Alternative with AutoGen.

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Debating Candidate Status via the MCP Server

The `list_candidates()` tool allows your agents to gather a full list of applicants. The multi-agent system then has agents debate which candidates pose the highest risk or offer the best fit. For example, one agent might flag candidates who haven't moved in weeks, while another recommends those with specific skills pulled from `get_candidate()`.

Comparing Job Requirements across Agents

Agents can use `list_jobs()` to get all open roles. They then debate the best fit by comparing job requirements against internal candidate data gathered using `get_job()`. This leads to a consensus on priority openings. This is useful when multiple departments are arguing over which role needs immediate staffing attention.

Consensus on Hiring Stages with AutoGen

The agents use `list_stages()` and `get_job()` to argue about the best next step in the hiring process. They don't just list stages; they debate if the current stage is appropriate for the job title. This consensus-driven approach helps prevent procedural errors by forcing multiple perspectives onto the workflow.

Setup guide

Set up Workable Alternative 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 Workable Alternative 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="Workable Alternative_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Workable Alternative MCP in AutoGen

The agents call `list_candidates()` first. They then debate which candidates should be prioritized, using specific details from `get_candidate()` to challenge or support each other's conclusions.

Yes. The agents use `list_jobs()` and `get_job()` in a debate format. They discuss the scope, required skills, and priority of different job postings.

This server provides core recruitment records: candidate profiles (via `get_candidate`), active jobs, defined hiring stages (`list_stages()`), and recruiter lists.

The agents access the roster using `list_recruiters()`. They can then debate which specific recruiter is best equipped to handle a newly identified job opening.

Absolutely. It uses `get_candidate()` details as primary evidence points during the agentic debate, ensuring decisions are grounded in real-time applicant information.

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