Can the AI automatically log time for a specific project?

Yes! Use the `create_timesheet` tool. You'll need to provide the ProjectID, PersonID, and the duration to instantly log work hours in your Avaza account.

How do I update the status of a task through the AI?

Simply ask the agent to run the `update_task` tool with the TaskID. You can modify the title, description, or status to reflect the current progress of your work.

Does this integration allow for auditing recent customer invoices?

Yes. The `list_invoices` tool provides a clear overview of recent billing, helping you track payment statuses and financial health across your projects.

Avaza MCP Server for CrewAI 11 tools — connect in under 2 minutes

Name: Avaza
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 11 Tools Framework

Connect your CrewAI agents to Avaza through Vinkius, pass the Edge URL in the `mcps` parameter and every Avaza 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="Avaza Specialist",
    goal="Help users interact with Avaza effectively",
    backstory=(
        "You are an expert at leveraging Avaza 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 Avaza "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 11 available tools "
        "and what they can do."
    ),
)

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

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IAMAccess control

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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 Avaza MCP Server

Connect your Avaza account to any AI agent and manage your entire professional services lifecycle through natural conversation.

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

Project & Task Operations — Create, update, and list projects and tasks to keep your team aligned and on schedule
Smart Time Tracking — Log and audit timesheet entries for accurate resource management and billing
CRM & Financial Insights — Access company contacts and retrieve recent invoices for full visibility into project financials
Resource Coordination — Programmatically manage your professional services workflows and team allocations

The Avaza MCP Server exposes 11 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 Avaza to CrewAI via MCP

Follow these steps to integrate the Avaza 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 11 tools from Avaza

Why Use CrewAI with the Avaza MCP Server

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

Avaza + CrewAI Use Cases

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

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

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

Avaza MCP Tools for CrewAI (11)

These 11 tools become available when you connect Avaza to CrewAI via MCP:

create_project

Create a new project

create_task

Create a new task in a project

create_timesheet

Create a new timesheet entry

get_account_check

Verify Avaza account connection

get_project

Get details for a specific project

list_contacts

List company contacts and users

list_invoices

List recent invoices

list_projects

List all Avaza projects

list_tasks

List all Avaza tasks

list_timesheets

List timesheet entries

update_task

Update an existing task

Example Prompts for Avaza in CrewAI

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

"List all active projects in Avaza."

"Create a new task 'Prepare Financial Audit' in project ID 12345."

"Log 2 hours of work for today on project 'Website Redesign'."

Troubleshooting Avaza MCP Server with CrewAI

Common issues when connecting Avaza 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.

Avaza + CrewAI FAQ

Common questions about integrating Avaza 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.