How to Use the Ayanza MCP in LangChain

Q: How do I pass Ayanza tool outputs to a LangChain agent?

You install the MCP adapter and pull tools using client.gettools(). Pass this list directly to your agent constructor so it can run createtask or listprojects on its own.

Q: Can I monitor Ayanza API latency inside LangChain?

Yes, LangSmith tracks every call to listtasks or getproject in real time. You see the exact input parameters and JSON payloads passing through the MCP Server.

Run LangChain agents that directly build, organize, and update your Ayanza workspace through multi-step reasoning chains.

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Connect Ayanza MCP to LangChain

Create your Vinkius account to connect Ayanza 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 Ayanza with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain-Driven Workspace Updates

Your LangChain agent runs `create_task` and then immediately feeds that output into `update_task` to assign owners based on workspace context. This multi-step execution turns raw project data into structured team actions without manual handoffs. LangSmith traces every step of the chain so you see exactly how the agent resolved task dependencies. You get clear visibility into token usage and latency for every single Ayanza update.

Context-Aware Wiki Analysis via LangChain

This MCP Server exposes `list_wiki_pages` to feed your chain's reasoning engine with live team documentation. Your agent reads the wiki, extracts action items, and instantly runs `create_task` to keep your team aligned. You combine these Ayanza tools with your existing SQL databases or vector store integrations in a single LangChain pipeline. The agent decides when to pull wiki pages and when to write tasks based on the live data flow.

Dynamic Project Auditing

The agent executes `list_projects` and loops through every active project to identify overdue milestones. It uses `list_tasks` to gather the details and compiles a clean status update for your team. Because LangChain handles state dynamically, the agent remembers the user context from `get_me` across the entire run. You get an automated auditor that knows who you are and which tasks need immediate attention.

Setup guide

Set up Ayanza 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 Ayanza 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({
    "ayanza-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 Ayanza transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Ayanza. 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.

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Common questions about Ayanza MCP in LangChain

You install the MCP adapter and pull tools using `client.get_tools()`. Pass this list directly to your agent constructor so it can run `create_task` or `list_projects` on its own.

Yes, LangSmith tracks every call to `list_tasks` or `get_project` in real time. You see the exact input parameters and JSON payloads passing through the MCP Server.

The LangChain multi-server client aggregates tools from different endpoints into a single agent. This lets your agent coordinate Ayanza tasks alongside your other development tools.

Vinkius manages the connection details and provides a single token. Your code just points to the Vinkius endpoint, and the agent gets immediate access to tools like `list_users`.

Your wiki pages, task descriptions, and project lists stay inside an ephemeral V8 sandbox. Vinkius processes these tool calls without persisting your workspace data, ensuring zero-trust security.

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