How to Use the Typefully MCP in CrewAI

Q: Can I get draft details using Typefully and the CrewAI?

Yes. An analysis agent can use getdraftdetails to pull specific content data from a draft ID, which another agent then uses for modification via updatedraft.

Q: What data does Typefully touch when using the CrewAI?

The server handles user profile data (getuserprofile), drafts (including content text), and connected social account lists. The agent workflow controls access to these types of data.

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Connect Typefully MCP to CrewAI

Create your Vinkius account to connect Typefully 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 Typefully with CrewAI

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Coordinated Content Drafting

A Research Agent can use `get_user_profile` to gather background info. Then, a Writing Agent uses `create_draft` to draft the actual content. A Monitor Agent watches these steps. It ensures that after drafting, the Moderator Agent reviews and then calls `update_draft` before passing it off.

Autonomous Publishing Pipelines

The team can autonomously run a publishing sequence. First, they use `list_social_accounts` to confirm connectivity. Then, the Action Agent uses `schedule_content` or `publish_immediately`, depending on the required timing. The whole operation runs without human intervention.

Full Draft Lifecycle Management

To start a job, one agent calls `list_drafts` to see what's ready. Another agent might use `get_draft_details` for deep context. Once the content is stale or wrong, the team uses `delete_draft`. The shared memory keeps track of all these actions.

Setup guide

Set up Typefully 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 Typefully tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

CrewAI runs specialized agents that collaborate using Typefully tools. One agent might research the topic, another drafts it using `create_draft`, and a third publishes it.

Yes. An analysis agent can use `get_draft_details` to pull specific content data from a draft ID, which another agent then uses for modification via `update_draft`.

The server handles user profile data (`get_user_profile`), drafts (including content text), and connected social account lists. The agent workflow controls access to these types of data.

Yes, the team can manage complex publishing schedules using `schedule_content`. This is perfect for setting up multi-platform content drops over time.

The collaboration process starts by confirming access. The agent runs `list_social_accounts` to ensure all necessary channels are open for publishing.

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