How to Use the Yida MCP in LangChain

Q: Can Yida support deleting data when paired with LangChain?

Yes. You can execute a controlled deletion using deleterecord after ensuring you have verified the record's details via getrecorddetails. The MCP Server handles this secure operation.

Q: If I run a complex chain, how does Yida and LangChain handle state?

The client is stateless by default. You must use client.session() to maintain persistent context between multiple tool calls within your multi-step reasoning pipeline.

Build complex decision chains using Yida MCP Server with LangChain.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Yida MCP to LangChain

Create your Vinkius account to connect Yida 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.

GDPR Free for Subscribers

Setup Yida with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-Step Record Management via MCP Server

You can manage entire data lifecycles in a chain. For instance, your agent first calls `list_forms` to find the correct form ID; then it uses `get_form_schema` to verify required fields before finally invoking `create_record`. This sequence ensures all necessary steps happen in order.

Workflow and User Auditing for LangChain

Build pipelines that audit system data. To track changes, your agent calls `get_instance_timeline` on a workflow ID. Next, it can call `get_record_details` to grab the specific record data associated with that instance. This gives you full visibility into who did what and when.

Dynamic Data Aggregation with LangChain

The MCP Server allows for complex data merging across different entities. You can list all available users using `list_users`, then iterate through them, fetching their associated records via `get_record_details`. This pattern lets you aggregate a full dataset based on system user lists.

Setup guide

Set up Yida 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 Yida 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({
    "yida-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 Yida 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 Yida. 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

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Yida now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Yida MCP in LangChain

You maintain integrity by building sequential steps. Your agent can first call `get_form_schema` to validate input against the defined structure before running `create_record`. This prevents bad data from entering your system.

Your agent can interact with forms, records, workflows, and users. Specific tools include `list_forms`, `list_workflows`, and `list_users`. Everything accessed runs through the MCP Server.

Yes. You can execute a controlled deletion using `delete_record` after ensuring you have verified the record's details via `get_record_details`. The MCP Server handles this secure operation.

The client is stateless by default. You must use `client.session()` to maintain persistent context between multiple tool calls within your multi-step reasoning pipeline.

The server exposes structured application data, including user records (from `list_users`), form definitions (via `list_forms` and `get_form_schema`), and actual instance data.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript