How to Use the Reply.io MCP in CrewAI

Q: How can I filter Reply.io tools for specific CrewAI agents?

Use the toolfilter parameter in your MCP configuration. This lets you give your researcher agent access to listpeople while keeping pauseperson limited to your moderator agent.

Q: Can I use CrewAI to manage multiple Reply.io email accounts?

You can. By using the listemailaccounts tool, your agents can monitor the health of all your connected accounts and rebalance the load if needed.

Deploy autonomous agent crews to manage your Reply.io campaigns and lead lifecycle.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Reply.io MCP to CrewAI

Create your Vinkius account to connect Reply.io to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Reply.io with CrewAI

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Autonomous Reply.io crews

Assign a dedicated agent in your CrewAI setup to monitor campaign stats. By calling `get_campaign_stats` on a schedule, the agent can decide when to scale up or pause your outreach. It turns your sales operations into a self-managing loop. The agent acts on the data it finds, freeing you to focus on high-level strategy instead of monitoring dashboards.

Collaborative Reply.io lead management

Use a team of agents where one researcher gathers prospect details and another uses `add_person` to start the sequence. CrewAI handles the handoff, ensuring the process is consistent. Each agent has a specific role, preventing overlap. They work together to move leads through your funnel without manual intervention.

Reply.io sequence monitoring with CrewAI

Your moderator agent can use `list_people` to perform regular audits of who is in which campaign. If it finds discrepancies, it triggers the necessary changes via the Reply.io tools. This keeps your list hygiene high. You define the criteria for a clean list, and the CrewAI agents enforce those rules automatically.

Setup guide

Set up Reply.io 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 Reply.io tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Use the tool_filter parameter in your MCP configuration. This lets you give your researcher agent access to `list_people` while keeping `pause_person` limited to your moderator agent.

Yes, CrewAI uses shared memory. Once an agent retrieves campaign data using `get_campaign`, the other agents in the crew can access that information to inform their next moves.

The most stable way is to pass the Vinkius URL directly to the agent's mcp list. This connects the agent to your Reply.io account instantly.

You can. By using the `list_email_accounts` tool, your agents can monitor the health of all your connected accounts and rebalance the load if needed.

Your credentials are never stored by the server. Every request is processed in an isolated sandbox, meaning your prospect contact data is only exposed to the agents you explicitly authorize.

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