How to Use the Wolai MCP in CrewAI

Q: How does CrewAI help me manage Wolai pages?

A specialized agent can use listpages to index all content, and another can then use getpage to retrieve the detailed contents of any specific page. The agents work in sequence to map out your entire information architecture.

Q: Is listing users reliable when using CrewAI?

The listusers tool is designed for autonomous operations. It provides a definitive list of workspace members, which the crew can use to enforce role-based access control across all actions.

Run autonomous operations on Wolai data using the CrewAI multi-agent framework.

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Connect Wolai MCP to CrewAI

Create your Vinkius account to connect Wolai 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 Wolai with CrewAI

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Autonomous Monitoring of Wolai Content with CrewAI

You can assign a dedicated monitor agent to watch for content gaps. This agent uses `list_blocks` on key pages and, if certain blocks are missing, triggers an alert via the shared memory. No human intervention is needed. The system could have one specialized agent run `get_workspace_info` periodically to ensure the overall organizational status remains accurate.

Coordinated Wolai Data Structuring with CrewAI

Build a team where Agent A calls `list_databases`, and Agent B takes that list, running `get_database` on each one to validate the schema. The agents share this validated structure as memory for later use. A separate agent can then run `create_database_row` based on inputs gathered from multiple sources, completing a full data record autonomously.

Executing Wolai Page Lifecycle with CrewAI

A specialized 'Writer' agent uses the tool `get_page` to pull existing content. A second 'Reviewer' agent then calls `list_blocks` and compares the list against a required template, flagging inconsistencies. The system can automate basic maintenance by having an agent call `create_page` when a new project is initiated in Wolai.

Setup guide

Set up Wolai 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 Wolai tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

A specialized agent can use `list_pages` to index all content, and another can then use `get_page` to retrieve the detailed contents of any specific page. The agents work in sequence to map out your entire information architecture.

Yes. You assign a 'Data Analyst' role and give it access to `query_database`. This agent retrieves the specific rows needed, and its findings are then passed to another agent for analysis.

The agents collaborate to build a full picture. One might use `list_databases` to identify all sources, while the main team coordinates calls to populate and update specific records using `create_database_row`.

The `list_users` tool is designed for autonomous operations. It provides a definitive list of workspace members, which the crew can use to enforce role-based access control across all actions.

The server handles both content and identity records. Specifically, it manages `page details` and the full `user list`, giving the agents visibility into who owns what in Wolai.

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