How do I get an Eden AI API Key?

Log in to your Eden AI account, navigate to **Account Settings** or the **API** section, and you can generate or retrieve your unique API Key from there.

Does the integration support all AI providers?

Yes, Eden AI provides a unified interface for many providers. The agent can retrieve availability and pricing for any provider currently supported by the Eden AI platform.

Can the agent trigger workflow executions?

This integration currently focuses on listing and auditing workflows, providers, and usage. Triggering new workflow executions should be managed via specific Eden AI API calls or the platform dashboard.

Eden AI MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Eden AI
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Eden AI through Vinkius, pass the Edge URL in the `mcps` parameter and every Eden AI tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Eden AI Specialist",
    goal="Help users interact with Eden AI effectively",
    backstory=(
        "You are an expert at leveraging Eden AI 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 Eden AI "
        "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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* 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 Eden AI MCP Server

Integrate Eden AI, the unified AI API platform, directly into your AI workflow. Manage your automation workflows and pipelines, track available AI providers (OpenAI, Google, AWS, etc.) across various features, monitor real-time API usage and costs, and oversee your LLM models using natural language.

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

What you can do

Workflow Oversight — List and retrieve detailed information and status for all your configured AI automation workflows.
Provider Intelligence — Access the provider registry to monitor available AI capabilities, pricing, and service levels for specific features.
Usage Monitoring — Track real-time API consumption statistics, credit balance, and organizational spending across all providers.
Model Management — List all specific large language models (LLMs) and AI features supported by the Eden AI platform.

The Eden AI 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 Eden AI to CrewAI via MCP

Follow these steps to integrate the Eden AI 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 Eden AI

Why Use CrewAI with the Eden AI MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Eden AI 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 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

Eden AI + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Eden AI MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Eden AI 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 Eden AI, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Eden AI 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 Eden AI against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Eden AI MCP Tools for CrewAI (10)

These 10 tools become available when you connect Eden AI to CrewAI via MCP:

get_ai_feature_pricing

Identify the pricing for a specific AI feature across different providers

get_api_usage_statistics

Retrieve technical statistics on your API usage and costs

get_eden_ai_metadata

Retrieve metadata and credit balance for your Eden AI account

get_workflow_configuration

Get detailed settings and steps for a specific AI workflow

list_ai_providers

List all AI providers (OpenAI, Google, AWS, etc.) available for a specific feature

list_ai_workflows

List all AI automation workflows configured in your Eden AI account

list_all_llm_models

List all specific large language models available through the unified API

list_available_ai_features

List all AI features and subfeatures supported by the Eden AI platform

list_latest_ai_automations

Identify the most recently updated AI workflows

quick_ai_provider_audit

Retrieve a high-level summary of available providers for text analysis

Example Prompts for Eden AI in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Eden AI immediately.

"List all active AI workflows."

"Show me the pricing for 'sentiment_analysis' across providers."

"What is my current Eden AI credit balance?"

Troubleshooting Eden AI MCP Server with CrewAI

Common issues when connecting Eden AI 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

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

Eden AI + CrewAI FAQ

Common questions about integrating Eden AI 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.