How to Use the BrandMentions MCP in CrewAI

Q: Can a CrewAI team run on-the-spot BrandMentions searches automatically?

Yes, a CrewAI agent can trigger postsearch when it detects a new trending topic. It then coordinates with other agents to poll getprocessedmentions and analyze the incoming social data.

Q: How do I restrict which BrandMentions MCP tools a CrewAI agent can use?

You can use the MCPServerHTTP class in CrewAI with a toolfilter to limit tool access. This ensures your researcher agent can read data with getprojectmentions but cannot delete campaigns via deleteproject.

Q: What happens if a CrewAI agent runs out of BrandMentions credits?

Your coordinator agent can run getremainingcredits at the start of a task. If credits are exhausted, the agent halts execution and alerts the human supervisor instead of wasting LLM tokens on failed requests.

Equip your CrewAI agent teams with the BrandMentions MCP Server to automate social listening.

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Connect BrandMentions MCP to CrewAI

Create your Vinkius account to connect BrandMentions 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 BrandMentions with CrewAI

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Collaborative Social Listening with CrewAI

The `get_project_mentions` tool provides the raw data feed that your CrewAI researcher agent uses to monitor brand sentiment. Once the researcher pulls the latest BrandMentions posts, a separate CrewAI analyst agent categorizes the feedback and drafts responses. This CrewAI setup functions entirely without human intervention to process BrandMentions data. By isolating the data collection to a specialized role, your CrewAI team processes large volumes of BrandMentions data without hitting context window bottlenecks.

Multi-Agent Influencer Outreach via MCP Server

The `get_influencers` tool allows your CrewAI discovery agent to identify high-impact BrandMentions creators for any active campaign. Once identified, a CrewAI writer agent drafts personalized outreach emails while a coordinator agent schedules follow-ups based on BrandMentions stats. If the CrewAI discovery agent needs to verify active campaigns first, it calls `list_projects` to map out the current BrandMentions setup. This collaborative CrewAI pipeline ensures your outreach strategy aligns perfectly with active BrandMentions tracking.

Autonomous Campaign Lifecycle Management

The `add_project` tool enables your CrewAI planner agent to spin up new BrandMentions tracking campaigns using the MCP tools. The CrewAI agent constantly monitors credit limits using `get_remaining_credits` to ensure the team has enough BrandMentions budget to execute the search. When a campaign ends, a CrewAI cleanup agent calls `delete_project` to keep your BrandMentions workspace organized. This autonomous CrewAI lifecycle management keeps your operations focused and prevents wasted BrandMentions credits on dead search terms.

Setup guide

Set up BrandMentions 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 BrandMentions tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent BrandMentions transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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="BrandMentions Analyst",
        goal="Access and analyze BrandMentions data via MCP.",
        backstory="Expert analyst with direct BrandMentions access.",
        tools=mcp_tools,
    )

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

CrewAI agents use shared memory to pass brand data, like lists retrieved from `get_project_mentions`, between specialized roles. The researcher agent pulls the data, and the analyst agent immediately reads it from the shared memory space.

Yes, a CrewAI agent can trigger `post_search` when it detects a new trending topic. It then coordinates with other agents to poll `get_processed_mentions` and analyze the incoming social data.

You can use the `MCPServerHTTP` class in CrewAI with a `tool_filter` to limit tool access. This ensures your researcher agent can read data with `get_project_mentions` but cannot delete campaigns via `delete_project`.

Your coordinator agent can run `get_remaining_credits` at the start of a task. If credits are exhausted, the agent halts execution and alerts the human supervisor instead of wasting LLM tokens on failed requests.

All API keys, brand mentions, and influencer profiles are processed in memory within Vinkius's secure, ephemeral V8 isolate sandboxes. CrewAI connects to the MCP Server over an encrypted endpoint, ensuring your proprietary search metrics never touch external persistent storage.

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