How to Use the Avaza MCP in LangChain

Q: Can my LangChain agent create Avaza tasks automatically?

Yes. The agent uses the createtask tool when it determines new work is required. You control the logic that triggers this action.

Q: How do I track token usage for Avaza API calls?

LangSmith handles this out of the box. Every call to listtimesheets or createproject appears as a distinct span in your trace.

Connect LangChain to Avaza and build multi-step chains that track time, manage projects, and pull invoices automatically.

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Connect Avaza MCP to LangChain

Create your Vinkius account to connect Avaza to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Avaza with LangChain

Ask AI about this MCP

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Claude

Perplexity

LangChain ReAct Agents for Avaza

Your ReAct agent decides what to do next based on live project data. You pass the `list_projects` and `get_project` tools into your agent executor. The model checks current project statuses before deciding where to allocate resources. Once it finds a bottleneck, the chain moves to action. It calls `create_task` to assign work to specific team members. Every step gets logged in LangSmith, so you see exactly which tool fired and how many tokens it burned.

Automate Time and Billing

Hooking up billing workflows requires precise execution. Your chain pulls recent billing data using `list_invoices` to check client balances. If an account is past due, the agent halts further work allocation. For active accounts, the system logs hours directly. It triggers `create_timesheet` after completing a chain step. You get automated time tracking without anyone touching a keyboard.

Manage Operations via MCP Server

Connecting this MCP Server gives your agent access to your entire contact database. It runs `list_contacts` to find the right stakeholder for a project update. The agent then formats a summary report based on recent activity. Task management happens in real time. When a client replies to an email, your chain parses the sentiment and fires `update_task` to change priorities. The integration runs stateless by default, but you can persist context across sessions.

Setup guide

Set up Avaza MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Avaza tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "avaza-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Avaza transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Avaza. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

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Common questions about Avaza MCP in LangChain

Install the langchain-mcp-adapters package. Pass your Vinkius endpoint into MultiServerMCPClient and pass the resulting tools to your ReAct agent.

Yes. The agent uses the create_task tool when it determines new work is required. You control the logic that triggers this action.

It works natively. You map the tools to your LangGraph nodes so the agent can loop through get_project and update_task until a condition is met.

LangSmith handles this out of the box. Every call to list_timesheets or create_project appears as a distinct span in your trace.

Your financial records stay within your Vinkius isolated sandbox. The server reads list_invoices data into memory just long enough for the agent to process the prompt, then the sandbox destroys the instance.

Use it with your favorite AI tools

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