Where do I find my Anvyl Team ID?

You can find your Team ID in the URL when you are logged into the Anvyl dashboard (e.g., `app.anvyl.com/teams/{TEAM_ID}/...`) or in your workspace settings.

Can I update the status of a production milestone via AI?

Yes, you can use the `confirm_milestone` tool to mark a milestone as complete or `delay_milestone` if production is behind schedule, providing a reason and a new expected date.

How do I track the shipping status of my orders?

Use the `list_logistics` tool with your Purchase Order ID. It will return all tracking records and shipping updates associated with that specific order.

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

Name: Anvyl
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Anvyl through Vinkius, pass the Edge URL in the `mcps` parameter and every Anvyl 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

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Anvyl Specialist",
    goal="Help users interact with Anvyl effectively",
    backstory=(
        "You are an expert at leveraging Anvyl 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 Anvyl "
        "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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DLPData protection

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Ed25519Audit chain

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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 Anvyl MCP Server

The Anvyl MCP Server brings visibility and automation to your supply chain operations. By connecting your Anvyl account to your AI agent, you can seamlessly track production progress, manage parts and suppliers, and update critical milestones using natural language.

When paired with CrewAI, Anvyl becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Anvyl 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 Visibility — List all purchase orders and drill down into specific order details to check status and quantities.
Milestone Management — Track production and shipping milestones. Confirm completions or record delays directly from your chat.
Supplier Coordination — Quickly retrieve supplier information and part specifications stored in Anvyl.
Logistics Tracking — Access tracking records and logistics data for any purchase order to keep your team informed on delivery timelines.

The Anvyl 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 Anvyl to CrewAI via MCP

Follow these steps to integrate the Anvyl 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 Anvyl

Why Use CrewAI with the Anvyl MCP Server

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

Anvyl + CrewAI Use Cases

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

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

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

Anvyl MCP Tools for CrewAI (10)

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

confirm_milestone

Confirm a milestone for a purchase order

delay_milestone

Delay a milestone for a purchase order

get_part

Get details for a specific part

get_purchase_order

Get details for a specific purchase order

get_supplier

Get details for a specific supplier

list_logistics

List tracking records for a purchase order

list_milestones

List milestones for a purchase order

list_parts

List parts in the Anvyl account

list_purchase_orders

List Anvyl purchase orders for the team

list_suppliers

List suppliers in the Anvyl account

Example Prompts for Anvyl in CrewAI

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

"List all active purchase orders."

"Check the milestones for order PO-123."

"Delay milestone 'm_456' for order PO-789 by 1 week because of raw material shortage."

Troubleshooting Anvyl MCP Server with CrewAI

Common issues when connecting Anvyl 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.

Anvyl + CrewAI FAQ

Common questions about integrating Anvyl 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.