How to Use the Workable Alternative MCP in CrewAI

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Connect Workable Alternative MCP to CrewAI

Create your Vinkius account to connect Workable Alternative to CrewAI 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 Workable Alternative with CrewAI

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Collaborative Candidate Management with CrewAI

Let a specialized agent use `get_candidate` to pull data. Then, have a second agent analyze that profile against current job requirements retrieved via `get_job`. The agents share memory and pass the findings to a third moderator agent for final decision-making. This isn't just calling tools; it's simulating an entire team of HR professionals working together on one task.

Listing Job Postings via MCP Server

`list_jobs` gives the initial data set. You can assign Agent A to research all postings, and then Agent B takes over to categorize them based on seniority level. The whole process runs sequentially within your CrewAI framework. The agents work in a pipeline: one collects, another filters, and a third reports. It’s designed for autonomous operations.

Specialized Agent Roles with CrewAI

You can define different roles using tools like `list_recruiters` or `list_stages`. One agent is the 'Recruiter Listener' (using `list_recruiters`), and another is the 'Process Manager' (using `list_stages`). They pass information back and forth until a goal is met. CrewAI handles the shared memory, so every agent knows what the others have found or decided.

Setup guide

Set up Workable Alternative MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Workable Alternative tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Workable Alternative Analyst",
    goal="Access and analyze Workable Alternative data via MCP.",
    backstory="Expert analyst with direct Workable Alternative access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Workable Alternative transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Workable Alternative Analyst",
        goal="Access and analyze Workable Alternative data via MCP.",
        backstory="Expert analyst with direct Workable Alternative access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Workable Alternative transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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Common questions about Workable Alternative MCP in CrewAI

You pass the MCP server URL directly to your agents. The specialized crew can then use tools like `list_candidates` as one of their capabilities. Because it's a multi-agent setup, it’s much more complex than calling a single tool.

Yes. You can assign a dedicated 'Monitor Agent.' This agent watches the entire session—the research, analysis, and action steps—while the rest of your crew works autonomously. It gives you visibility into every decision.

The server touches candidate profiles, job postings, recruiter lists, and hiring stages. CrewAI's strength is processing these varied structured records across multiple specialized agents.

CrewAI provides the architecture. You define the roles and goals, and the framework manages the complex interactions between tools like `get_candidate` across multiple agents without you having to write all the state management code.

The server touches candidate records, job postings, and recruiter profiles. Since it's a collaboration of specialized agents, always ensure that the scope of each agent is limited to only the data types they absolutely need.

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