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 Pydantic AI 11 tools — connect in under 2 minutes

Name: Avaza
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 11 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Avaza through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Avaza "
            "(11 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Avaza?"
    )
    print(result.data)

asyncio.run(main())

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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.

Pydantic AI validates every Avaza tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Avaza MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 11 tools from Avaza with type-safe schemas

Why Use Pydantic AI with the Avaza MCP Server

Pydantic AI provides unique advantages when paired with Avaza through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Avaza integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Avaza connection logic from agent behavior for testable, maintainable code

Avaza + Pydantic AI Use Cases

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

Type-safe data pipelines: query Avaza with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Avaza tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Avaza and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Avaza responses and write comprehensive agent tests

Avaza MCP Tools for Pydantic AI (11)

These 11 tools become available when you connect Avaza to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Avaza to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Avaza + Pydantic AI FAQ

Common questions about integrating Avaza MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Avaza MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.