How to Use the Clarifai (Vision AI) MCP in CrewAI

Q: How do I add Clarifai (Vision AI) to a CrewAI agent?

Install crewaitools. Add your Vinkius endpoint URL directly to the mcps array in your agent definition to expose the visual analysis capabilities.

Q: Can I restrict which tools my agents can access?

Use MCPServerHTTP from crewai.mcp and apply a toolfilter. This prevents a researcher agent from accidentally triggering heavy inference tasks.

Deploy autonomous computer vision teams using CrewAI and specialized image analysis agents.

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Connect Clarifai (Vision AI) MCP to CrewAI

Create your Vinkius account to connect Clarifai (Vision 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 Clarifai (Vision AI) with CrewAI

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Assign Vision Tasks via MCP Server

`predict_model` parses exactly what the evaluated AI limit bounded for image classifications. You assign this tool to a specialized analyst agent in your CrewAI setup, giving it the ability to see and categorize visual inputs. You don't need a human copying links. A separate researcher agent gathers image URLs from the web. It passes those links to the analyst, who runs the inference and hands the classification results to a reporting agent for final documentation.

Discover and Map Visual Concepts

`list_concepts` extracts explicitly attached semantic bounds tagging your datasets. A taxonomy agent uses this operation to understand the exact vocabulary the computer vision system uses before writing its analysis. `list_datasets` identifies the precise physical bounds mapping your data structures. The crew maps out the available training sets autonomously, deciding which data pool offers the best match for the current visual task.

Audit Available Models and Workflows

`list_models` performs structural extraction of computer vision parameters driving the features. A manager agent queries this to select the right model architecture before delegating the prediction task to a subordinate. `list_workflows` retrieves the exact structural matching verifying chained operations. Meanwhile, `list_apps` identifies the bounded applications managing global compute limits, preventing the crew from exhausting resources during bulk processing.

Setup guide

Set up Clarifai (Vision 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 Clarifai (Vision AI) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Clarifai (Vision 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 Clarifai. 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 Clarifai (Vision AI) MCP in CrewAI

Install `crewai[tools]`. Add your Vinkius endpoint URL directly to the `mcps` array in your agent definition to expose the visual analysis capabilities.

Yes. You assign specific tools to different roles. The manager agent might handle the listing tools while the analyst agent handles the actual prediction calls.

A top-level manager agent evaluates the user request, queries the available models, and instructs a worker agent to execute the prediction tool on the target image.

Use `MCPServerHTTP` from `crewai.mcp` and apply a `tool_filter`. This prevents a researcher agent from accidentally triggering heavy inference tasks.

Image URLs and resulting concept tags pass through an ephemeral V8 Isolate Sandbox. The Vinkius infrastructure processes the inference request and instantly destroys the memory state, ensuring your visual data never persists.

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