How to Use the Ninehire MCP in CrewAI

Q: How do I limit which Ninehire tools my CrewAI agents can access?

Use the toolfilter option in MCPServerHTTP to restrict access. You can expose only read tools like listjobpostings to researcher agents, keeping registration tools protected.

Deploy autonomous recruiting teams using CrewAI and Ninehire.

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Connect Ninehire MCP to CrewAI

Create your Vinkius account to connect Ninehire 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 Ninehire with CrewAI

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Run multi-agent recruitment drives with CrewAI

Let a crew of specialized agents manage your talent pipeline without manual intervention. Your researcher agent can look up open positions with `list_job_postings`, while an analyst agent evaluates candidates. This MCP Server enables simple coordination across your crew. By sharing memory, one agent can fetch candidates via `list_job_applicants` while another agent looks up specific profiles using `get_applicant_profile`.

Coordinate hiring departments and teams with this MCP Server

Stop manually routing resumes to different managers. This MCP Server lets your CrewAI agents fetch company structures via `list_organization_departments` and assign candidates to the correct team. The moderator agent reads the output of `list_hiring_teams` to identify who should review a candidate. It then schedules evaluations without requiring human intervention to map departments.

Automate applicant screening with specialized agents

Set up a hierarchical execution flow where a screening agent gathers data and a supervisor agent makes the final decision. The screening agent uses `get_job_details` to understand the role requirements. Once the requirements are clear, the agent pulls candidate feedback using `list_candidate_evaluations`. The supervisor agent reviews this aggregated data to decide if the candidate moves forward.

Setup guide

Set up Ninehire 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 Ninehire tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Ninehire 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="Ninehire Analyst",
        goal="Access and analyze Ninehire data via MCP.",
        backstory="Expert analyst with direct Ninehire access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Ninehire 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 Ninehire. 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 Ninehire MCP in CrewAI

You can pass the Vinkius HTTP URL directly into the agent's `mcps` list. For more granular control, use the MCP integration from the `crewai.mcp` package to filter specific tools.

Yes, using shared memory, one agent can fetch candidate scores using `list_candidate_evaluations` while a writer agent drafts feedback based on those scores.

Use the `tool_filter` option in `MCPServerHTTP` to restrict access. You can expose only read tools like `list_job_postings` to researcher agents, keeping registration tools protected.

The integration supports stdio, SSE, and Streamable HTTP transports. For CrewAI setups, Streamable HTTP is typically the easiest to configure with your Vinkius endpoint.

All personal applicant details like names and emails processed during `register_new_applicant` pass through a cryptographically isolated gateway. Vinkius never retains this data on disk, keeping your recruitment pipeline completely secure.

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