Can I check the progress of my image generation through my agent?

Yes. Use the `get_generation_status` tool with the Generation ID provided when you started the request. Your agent will poll the Leonardo API and return the final image URLs and metadata once the process is complete.

How do I find which AI models are available for generation?

Ask your agent to `list_platform_models` or `list_custom_models`. It will return a list of available UUIDs and model names (like Phoenix or Kino XL), which are required when triggering a new image generation request.

Can my agent perform image-to-image transformations?

Absolutely. Use the `upload_init_image` tool to acquire a secure presigned URL for your source image. Once uploaded, you can command your agent to trigger a generation that uses that image as a reference for guided AI transformations.

Leonardo.ai (Generative AI & Models) MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Leonardo.ai (Generative AI & Models)
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Leonardo.ai (Generative AI & Models) through the Vinkius — pass the Edge URL in the `mcps` parameter and every Leonardo.ai (Generative AI & Models) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Leonardo.ai (Generative AI & Models) Specialist",
    goal="Help users interact with Leonardo.ai (Generative AI & Models) effectively",
    backstory=(
        "You are an expert at leveraging Leonardo.ai (Generative AI & Models) tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Leonardo.ai (Generative AI & Models) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Leonardo.ai (Generative AI & Models) MCP Server

Connect your Leonardo.ai account to any AI agent and take full control of state-of-the-art generative image production and custom AI models through natural conversation.

When paired with CrewAI, Leonardo.ai (Generative AI & Models) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Leonardo.ai (Generative AI & Models) tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

What you can do

Generation Orchestration — Initiate asynchronous image generation requests defining precise prompts, model UUIDs, and dimensions directly from your agent
Model Discovery — Enumerate global platform models (Phoenix, Kino XL) and your fine-tuned custom models to understand available inference capabilities
Image-to-Image — Acquire secure presigned URLs to upload initial images for guided AI generation and reference-based transformations
Precision Variations — Create unzoom context extensions and visual variations expanding previously generated images while maintaining structural consistency
Inventory Audit — List recent user generations and retrieve absolute image URLs, prompts used, and exact hardware metadata securely
User Metrics — Monitor active account metrics and token usage allocations to manage your generation budget and operational costs in real-time

The Leonardo.ai (Generative AI & Models) MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Leonardo.ai (Generative AI & Models) to CrewAI via MCP

Follow these steps to integrate the Leonardo.ai (Generative AI & Models) MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from Leonardo.ai (Generative AI & Models)

Why Use CrewAI with the Leonardo.ai (Generative AI & Models) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Leonardo.ai (Generative AI & Models) through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Leonardo.ai (Generative AI & Models) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Leonardo.ai (Generative AI & Models) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Leonardo.ai (Generative AI & Models) for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Leonardo.ai (Generative AI & Models), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Leonardo.ai (Generative AI & Models) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Leonardo.ai (Generative AI & Models) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Leonardo.ai (Generative AI & Models) MCP Tools for CrewAI (10)

These 10 tools become available when you connect Leonardo.ai (Generative AI & Models) to CrewAI via MCP:

create_variation

Create an unzoom context extension expanding a Leonardo.ai generated image

delete_generation

Delete a Leonardo generation history log and its image array explicitly

generate_image

Returns a Generation ID used to poll for the output. Generate images from a text prompt using Leonardo.ai

get_generation

Get the active status or completed result of a generation

get_model

Get specific details and parameters of a Leonardo.ai model

get_user

Get active authenticated Leonardo AI user metrics

list_custom_models

List fine-tuned and custom-trained models available explicitly on your Leonardo instance

list_platform_models

List all global public platform models hosted on Leonardo.ai

list_user_generations

List recent image generations initiated by a specific Leonardo user

upload_init_image

Acquire a secure presigned URL tracking for image-to-image inference datasets

Example Prompts for Leonardo.ai (Generative AI & Models) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Leonardo.ai (Generative AI & Models) immediately.

"Generate a futuristic cityscape at sunset using the Phoenix model"

"List my last 3 image generations"

"Check my current token balance and account limits"

Troubleshooting Leonardo.ai (Generative AI & Models) MCP Server with CrewAI

Common issues when connecting Leonardo.ai (Generative AI & Models) to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Leonardo.ai (Generative AI & Models) + CrewAI FAQ

Common questions about integrating Leonardo.ai (Generative AI & Models) MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.