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 LangChain 11 tools — connect in under 2 minutes

Name: Avaza
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 11 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Avaza through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "avaza": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Avaza, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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.

LangChain's ecosystem of 500+ components combines seamlessly with Avaza through native MCP adapters. Connect 11 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Avaza MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 11 tools from Avaza via MCP

Why Use LangChain with the Avaza MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Avaza MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Avaza queries for multi-turn workflows

Avaza + LangChain Use Cases

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

RAG with live data: combine Avaza tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Avaza, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Avaza tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Avaza tool call, measure latency, and optimize your agent's performance

Avaza MCP Tools for LangChain (11)

These 11 tools become available when you connect Avaza to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Avaza + LangChain FAQ

Common questions about integrating Avaza MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.