How to Use the Mention MCP in CrewAI

Q: How does Mention interact with CrewAI memory?

The results from listmentions are stored in the crew's shared memory. Subsequent agents use this stored context to make smarter decisions without re-querying the API.

Q: Is it possible to filter by alert statistics in CrewAI?

Yes. You add a step where an agent calls getalertstatistics first. Based on the numbers, the crew decides whether to dive deeper into the mentions or move on.

Deploy a specialized crew of agents to monitor brand sentiment using the Mention MCP Server.

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Connect Mention MCP to CrewAI

Create your Vinkius account to connect Mention 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 Mention with CrewAI

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Multi-agent research with CrewAI

Assign one agent to `list_mentions` while another specializes in `get_mention_details`. This allows your crew to divide the labor between finding new posts and analyzing them. Share context across your crew. Because CrewAI maintains memory, your analyst agent will know exactly which posts the researcher agent flagged as important.

Autonomous PR monitoring for CrewAI

Set up a monitor agent that periodically calls `list_alerts`. If a new alert appears, the agent can hand it off to a moderator agent for immediate action. Focus your agents on high-value data. Use `list_favorite_mentions` to ensure the team is only looking at the most relevant social interactions regarding your brand.

Scale social operations with CrewAI

Expand your monitoring coverage by adding more agents to the crew. Each one uses the same `search_mentions` tool to query social data for different keywords simultaneously. Keep operations lean. By using this MCP Server, you avoid building custom API wrappers for your agents, letting the crew focus on the actual logic of your social strategy.

Setup guide

Set up Mention 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 Mention tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You provide the MCP Server URL in the agent configuration. CrewAI automatically discovers all 10 tools, making them available to your research and analysis agents.

The server restricts access to only what the tools expose. Your agents only see the specific mention and alert details you explicitly ask them to retrieve.

The results from `list_mentions` are stored in the crew's shared memory. Subsequent agents use this stored context to make smarter decisions without re-querying the API.

Yes. You add a step where an agent calls `get_alert_statistics` first. Based on the numbers, the crew decides whether to dive deeper into the mentions or move on.

It supports SSE and HTTP. Most teams use the HTTP transport for its reliability when running autonomous crews that need to stay online 24/7.

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