How to Use the CONSENSUS MCP in CrewAI

Q: How do I limit which CONSENSUS MCP tools a CrewAI agent can use?

Use the MCPServerHTTP class from crewai.mcp and apply a toolfilter. This lets you restrict a junior agent to only running listdemos while keeping administrative tools like listusers locked down.

Deploy specialized agent teams to track, analyze, and optimize your CONSENSUS demos using CrewAI and this MCP Server.

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Connect CONSENSUS MCP to CrewAI

Create your Vinkius account to connect CONSENSUS 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 CONSENSUS with CrewAI

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Perplexity

Multi-agent coordination for sales pipelines

Calling `list_invitations` allows your monitoring agent to identify inactive links and coordinate with follow-up agents. Set up a dedicated crew where one agent monitors invite status and another builds the follow-up strategy. The strategist agent then queries `list_demos` to find a more relevant feature video. By using this MCP Server, your crew collaborates autonomously to swap out stale content and revive cold deals.

Automated buyer behavior analysis via CrewAI

Querying `list_demolytics` lets your analyst agent extract exact watch times and feature clicks from recent views. Let your crew digest complex engagement data without human intervention. The analyst then writes a summary and hands it to a writer agent, who drafts a personalized email based on the specific features the buyer spent the most time watching.

Team workload balancing and demoboard audits

Running `list_demoboards` enables your crew to find older, inactive boards and cross-reference them with your active team. Keep your sales team organized by letting an agent audit active boards. If a board has been abandoned, the agent assigns a cleanup task or alerts the owner from `list_users`. This keeps your customer portals clean and your active sales reps focused on hot prospects.

Setup guide

Set up CONSENSUS 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 CONSENSUS tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You can configure your CrewAI agent by passing your Vinkius endpoint URL directly in the `mcps` list parameter. The framework automatically discovers the tools, giving your agents access to operations like `list_demos` out of the box.

Yes, CrewAI agents share memory and context. If your researcher agent calls `get_demoboard`, the resulting viewer data is placed in the shared memory pool, allowing your analyst agent to process it without making another API call.

Use the `MCPServerHTTP` class from `crewai.mcp` and apply a `tool_filter`. This lets you restrict a junior agent to only running `list_demos` while keeping administrative tools like `list_users` locked down.

Yes, the integration fully supports stdio, SSE, and Streamable HTTP transports. You can run and test your agent crews locally on your machine before deploying them to production.

All demoboard configurations and viewer analytics are processed in ephemeral sandboxes that wipe clean after execution. No prospect contact info or engagement history is ever stored or logged by the gateway. Your data transmission is protected by industry-standard TLS encryption.

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