How to Use the AirMatch Pro MCP in CrewAI

Q: Can CrewAI agents update AirMatch Pro job listings autonomously?

While CrewAI agents can read active listings using listjobs, modifications are restricted to creating proposals and linking customers. This keeps your core AirMatch Pro pipeline safe from accidental agent edits.

Deploy specialized agent crews to manage your AirMatch Pro pipeline autonomously using CrewAI and MCP.

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Connect AirMatch Pro MCP to CrewAI

Create your Vinkius account to connect AirMatch Pro 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 AirMatch Pro with CrewAI

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Collaborative talent sourcing with this MCP Server

The `list_leads` tool enables your researcher CrewAI agent to gather fresh candidate profiles for the rest of the crew to analyze. Once the researcher finds an AirMatch Pro lead, a separate analyst agent uses `get_lead` to evaluate their background against active requisitions. This team-based CrewAI approach ensures that candidate evaluation happens in parallel. By dividing the labor, your crew handles high-volume AirMatch Pro recruitment pipelines without bottlenecking a single agent.

Autonomous proposal drafting in CrewAI squads

The `create_proposal` tool is used by your closer CrewAI agent to finalize client agreements after a successful match. The closer agent coordinates with a finance agent who pulls pricing details using `list_estimates` to ensure the AirMatch Pro numbers are accurate. Because CrewAI supports shared memory, the closer agent knows exactly which customer profile to link from `list_customers` inside the AirMatch Pro database. The entire contract lifecycle runs from start to finish over MCP without human intervention.

Automated pipeline audits and health checks

The `list_jobs` tool lets a supervisor CrewAI agent monitor active AirMatch Pro job boards and assign sourcing tasks to subordinate agents. The supervisor tracks which roles are lagging and shifts CrewAI focus to those open positions. To ensure the system stays online, the crew uses `check_airmatch_status` to verify AirMatch Pro api health before starting a run. If the connection is down, the supervisor pauses the CrewAI execution to prevent wasted API tokens.

Setup guide

Set up AirMatch Pro 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 AirMatch Pro tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

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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="AirMatch Pro Analyst",
        goal="Access and analyze AirMatch Pro data via MCP.",
        backstory="Expert analyst with direct AirMatch Pro access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent AirMatch Pro 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 →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AirMatch Pro. 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 AirMatch Pro MCP in CrewAI

Pass the filtered tool list to each CrewAI agent's configuration block. For example, give your researcher agent `list_leads` and your closer agent `create_proposal` to keep their AirMatch Pro roles distinct.

While CrewAI agents can read active listings using `list_jobs`, modifications are restricted to creating proposals and linking customers. This keeps your core AirMatch Pro pipeline safe from accidental agent edits.

CrewAI manages agent execution sequentially or hierarchically. This natural ordering prevents your agents from spamming `list_leads` simultaneously and triggering AirMatch Pro rate limits.

No, you only need the main CrewAI library and the Vinkius endpoint. The framework dynamically parses the AirMatch Pro server schema and exposes all ten tools to your crew.

Your AirMatch Pro customer profiles and proposal drafts remain inside your local Python execution environment. The Vinkius gateway uses zero-trust, ephemeral V8 sandboxes to process the CrewAI tool calls safely.

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