How to Use the Workvivo MCP in CrewAI

Q: How do I build a Workvivo report using CrewAI?

You assign roles: one agent lists all posts (listworkvivoposts), another gets details for specific items, and a third summarizes the output. The crew handles the sequence.

Run Specialized Agents Against Workvivo Data Using the CrewAI Framework.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Workvivo MCP to CrewAI

Create your Vinkius account to connect Workvivo 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.

GDPR Free for Subscribers

Setup Workvivo with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Autonomous Research and Gathering

Need a full overview of activity? Assign one agent to use `list_workvivo_posts` to gather all recent social posts. A second, specialized agent can then process that list, using `get_post_details` on specific items. This role-based specialization lets you build an automated report without having a human watch the session.

Targeted Data Cleanup

If content needs removal, assign a cleanup agent to use `delete_social_post`. This is critical for maintaining data integrity in Workvivo. You can also tell an agent to review and list all available spaces using `list_collaboration_spaces` before deciding which posts need attention.

Directory and Event Reporting

Use a reporting agent that combines data sources. One worker gets employee lists via `list_employees`, while another retrieves upcoming dates with `list_company_events`. The framework manages the shared memory, ensuring both reports are compiled against the same timeline or directory context.

Setup guide

Set up Workvivo 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 Workvivo tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Workvivo 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 Workvivo. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Workvivo now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Workvivo MCP in CrewAI

You assign roles: one agent lists all posts (`list_workvivo_posts`), another gets details for specific items, and a third summarizes the output. The crew handles the sequence.

Absolutely. You can assign an agent to list all available groups using `list_collaboration_spaces` and then another agent to check specific details using `get_space_details`.

The crew's shared memory is key. Agent A gathers the initial list of posts, and that result immediately becomes input for Agent B to analyze or act upon.

Assign a specific 'Moderator' agent whose sole job is running `delete_social_post`. Make sure this action is only run after multiple agents have confirmed the necessity.

You handle varied inputs, like a list of employee objects or structured social post content. The system processes `social_post` content for deletion and analysis.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript