How to Use the AntEater MCP in CrewAI

Q: How do I pass the AntEater MCP Server to a CrewAI agent?

You can pass your Vinkius URL directly into the agent's mcps parameter list. For granular control, import MCPServerHTTP and filter tools like searchcontacts before exposing them.

Q: Is my team's internal Slack and email search data secure?

Yes. All communication logs queried via searchactivity are processed inside isolated, ephemeral MCP sandboxes. Your data is never saved, stored, or used for training models.

Deploy specialized CrewAI agent teams to monitor AntEater activity feeds and manage shared contacts autonomously.

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Connect AntEater MCP to CrewAI

Create your Vinkius account to connect AntEater 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 AntEater with CrewAI

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Coordinate multi-agent research using this MCP Server

Equipping your research agent with `search_activity` allows it to dig through combined Slack and email histories. While one agent searches, an analyst agent uses the output to build a timeline of client interactions. This division of labor prevents context limits from choking your run. Each specialized agent only requests the specific chunks of history it needs to complete its assigned task.

Autonomous contact management for CrewAI teams

When running database audits, the `list_contacts` tool allows a dedicated manager agent to scan your shared directory. It coordinates with a writer agent that uses `get_contact_history` to merge overlapping records. Because CrewAI supports hierarchical execution, a moderator agent can review the proposed changes before they are finalized. This keeps your shared database accurate without requiring constant human oversight.

Real-time team coordination and workload monitoring

To coordinate workloads, the `get_user_activity` tool enables a supervisor agent to track individual team member tasks. It checks active tasks across your communication channels to balance assignments automatically. Combining this with `list_users` lets your crew map out who is online and available. Your agents route urgent tasks to free team members based on actual, live activity logs.

Setup guide

Set up AntEater 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 AntEater tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You can pass your Vinkius URL directly into the agent's `mcps` parameter list. For granular control, import `MCPServerHTTP` and filter tools like `search_contacts` before exposing them.

Yes. The server handles concurrent requests from different agents, meaning your researcher can run `search_activity` while your coordinator checks `get_user_activity` at the same time.

CrewAI reads the schema from the server endpoint. Your agents instantly understand the parameters needed for tools like `get_contact_history` without manual Python coding.

No. Vinkius manages the authentication layer, meaning your entire crew shares a single secure endpoint token to access the tools.

Yes. All communication logs queried via `search_activity` are processed inside isolated, ephemeral MCP sandboxes. Your data is never saved, stored, or used for training models.

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