How to Use the Vectorizer AI MCP in CrewAI

Q: Can I validate resources before starting a job with Vectorizer AI in CrewAI?

Yes. You configure an initial step where one agent uses getaccountstatus. This allows the entire crew to halt or adjust based on the credit status.

Q: Is vectorizing complex graphics possible with Vectorizer AI for CrewAI?

Yes, the vectorizeimage tool handles bitmap-to-vector conversion into formats like SVG and DXF. The agents just need to coordinate passing the correct source files.

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Create your Vinkius account to connect Vectorizer AI 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 Vectorizer AI with CrewAI

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Specialized vectorization tasks

Assign a dedicated 'Design Agent' to use the `vectorize_image` tool. This agent takes a bitmap input (JPG/PNG) and transforms it into high-quality vectors like SVG or PDF. The result is shared memory for other agents. This role-based specialization ensures that only the correct agent attempts the complex tracing operation.

Pre-flight resource validation

A 'Moderator Agent' can check system constraints by calling `get_account_status`. This prevents the team from starting a job that will fail due to insufficient credits or an expired subscription. It’s key for monitoring and escalation, flagging issues before they consume resources.

Secure final asset retrieval

Once the vectorization is complete, another agent can use `download_image` to retrieve the finished file. The team can then instruct a cleanup agent to run `delete_image`, managing both the output and temporary tokens. The shared memory structure keeps track of these assets throughout the session.

Setup guide

Set up Vectorizer AI 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 Vectorizer AI tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

The tools allow specialized agents to hand off work. For example, Agent A runs `vectorize_image`, and the resulting data is passed directly to Agent B for further analysis or export.

Yes. You configure an initial step where one agent uses `get_account_status`. This allows the entire crew to halt or adjust based on the credit status.

The server touches PNG or JPG inputs and associated temporary image tokens. These are tracked by the shared memory so that multiple agents know what resources are available.

Yes, the `vectorize_image` tool handles bitmap-to-vector conversion into formats like SVG and DXF. The agents just need to coordinate passing the correct source files.

The server manages the initial PNG or JPG inputs, along with temporary image tokens. These assets must be handled correctly throughout the autonomous operation sequence.

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