How to Use the Agify MCP in CrewAI

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Connect Agify MCP to CrewAI

Create your Vinkius account to connect Agify 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 Agify with CrewAI

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Assign age prediction tasks to your CrewAI agents

Give your research agent access to `predict_age`. It will call the tool whenever it encounters a name it needs to classify. Your agents share the result within the crew memory. Another agent can then take that age and adjust its own strategy accordingly.

Scale your CrewAI research with batch processing

Use `predict_age_batch` when your agents need to categorize large lists of names. It keeps your crew moving fast by reducing the number of individual tool calls. This efficiency prevents your agents from getting bogged down in repetitive requests. They finish the job and move to the next task.

Filter tools for specific CrewAI agents

Restrict which agents see the Agify tools. You might only want your Analyst agent to have access to age data. This keeps your crew organized. Only the agents that need the data can call the functions, keeping your operations clean.

Setup guide

Set up Agify 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 Agify tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the server URL in your agent configuration. Your crew will detect the tools and start using them automatically.

Yes, shared memory lets your team pass the age data between agents. The researcher finds the age, and the strategist uses it.

It integrates into any setup, whether you use sequential or hierarchical execution. Your agents collaborate on the data seamlessly.

Your server connection is private. Only agents within your defined crew have access to the tools and the data they return.

The server handles only first names. No records of these names are kept on our servers after the age is calculated.

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