How do I find my Afosto API credentials?

Log in to the Afosto Admin App, navigate to **Company** > **API Settings**. You can create a new integration there to generate your Client ID and Client Secret.

Can I check stock levels across multiple warehouses?

Yes! Use the `list_inventory` tool. It provides real-time stock data aggregated by location, helping you manage omnichannel fulfillment.

Does this support multi-channel orders?

Absolutely. The `list_orders` tool retrieves orders from all connected sales channels, including B2B, B2C, and integrated marketplaces.

Afosto MCP Server for CrewAI 4 tools — connect in under 2 minutes

Name: Afosto
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 4 Tools Framework

Connect your CrewAI agents to Afosto through Vinkius, pass the Edge URL in the `mcps` parameter and every Afosto 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="Afosto Specialist",
    goal="Help users interact with Afosto effectively",
    backstory=(
        "You are an expert at leveraging Afosto 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 Afosto "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 4 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 Afosto MCP Server

Connect your Afosto Retail Engine account to your AI agent to unlock enterprise-grade e-commerce orchestration. From managing multi-channel orders to monitoring real-time inventory across warehouses and auditing product catalogs, your agent handles your retail stack through natural conversation.

When paired with CrewAI, Afosto becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Afosto 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

Order Orchestration — List and audit multi-channel orders, check fulfillment statuses, and retrieve technical metadata
Inventory Management (WMS) — Monitor real-time stock levels across multiple locations and identify replenishment needs
Product Catalog Oversight — List products, variants, and pricing to ensure your storefront is always synchronized
Customer Intelligence — Retrieve centralized customer profiles and interaction history to support sales and support
Retail Insights — Quickly identify sales trends or inventory bottlenecks directly from your chat interface

The Afosto MCP Server exposes 4 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 Afosto to CrewAI via MCP

Follow these steps to integrate the Afosto 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 4 tools from Afosto

Why Use CrewAI with the Afosto MCP Server

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

Afosto + CrewAI Use Cases

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

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

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

Afosto MCP Tools for CrewAI (4)

These 4 tools become available when you connect Afosto to CrewAI via MCP:

list_customers

Retrieve centralized customer profile data and their historic eCommerce behavior

list_inventory

Retrieve real-time inventory and stock levels aggregated by warehouse distribution

list_orders

Retrieve recent multi-channel eCommerce orders aggregated by Afosto

list_products

Retrieve the complete product catalog, highlighting active variants and their pricing

Example Prompts for Afosto in CrewAI

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

"List all pending orders from the last 24 hours."

"What is the current stock level for 'Wireless Headphones' in all warehouses?"

"Search for a customer profile with email 'john.doe@example.com'."

Troubleshooting Afosto MCP Server with CrewAI

Common issues when connecting Afosto 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.

Afosto + CrewAI FAQ

Common questions about integrating Afosto 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.