How to Use the Qualified.io MCP in LangChain

Q: How do I run a multi-step candidate invite chain with Qualified.io in LangChain?

Use MultiServerMCPClient to connect the server to your LangChain setup. You can write a chain that calls listassessments to find the correct test ID, then feeds that ID directly into invitecandidates for the target candidate list.

Q: Can LangChain trace the tool calls made to the Qualified.io MCP Server?

Yes, every tool call like getassessmentresult or invitecandidatesviacohort is fully tracked in LangSmith. You get complete visibility into latency, payload size, and LLM reasoning steps for every single candidate interaction.

Q: How does LangChain handle failed candidate tests on Qualified.io?

Your LangChain agent can monitor test failures by calling listassessmentresults periodically. If a candidate ran into a technical glitch, the agent can automatically call scheduleretryassessmentresult to reopen the test.

Q: How is my candidate assessment data secured during LangChain runs?

All candidate code submissions and test scores pulled via getassessmentresultexhibit run through Vinkius's secure V8 isolates. The raw candidate data is never stored on the platform, and your API tokens are kept isolated from the LLM context.

Build multi-step screening pipelines in LangChain to invite candidates and track coding test results without leaving your code.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Qualified.io MCP to LangChain

Create your Vinkius account to connect Qualified.io to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup Qualified.io with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Automated candidate screening chains

The Qualified.io MCP Server lets your LangChain agent run candidate screening pipelines by combining multiple assessment tools into a single chain. Your agent starts by calling `list_assessments` to find the right test, then uses `invite_candidates` to email the applicant. LangSmith traces the entire sequence, showing you exactly when the tool was called and what response came back. This gives you full visibility into candidate outreach latency and token usage during high-volume hiring spikes.

Intelligent grading and review pipelines

This MCP setup connects candidate submissions to your LangChain evaluation chains using `get_assessment_result` to pull raw code submissions. Once the data is in your chain, the agent analyzes the code and uses `create_assessment_result_review` to log structured feedback. By feeding these outputs directly into your existing LangChain databases, you build a continuous feedback loop. You can even update existing reviews using `update_assessment_result_review` if a candidate submits a late correction.

Dynamic assessment management

Your LangChain agent can manage your entire test library using this MCP Server. It checks available challenges with `list_challenges`, builds a test via `create_assessment`, and publishes it to your portal with `publish_assessment`. If a test becomes outdated, the agent swaps it out by calling `archive_assessment` and `unpublish_assessment` in sequence. This keeps your active test library clean and ensures candidates only see relevant, up-to-date coding challenges.

Setup guide

Set up Qualified.io 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 Qualified.io 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({
    "qualifiedio-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 Qualified.io 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 Qualified.io. 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 Qualified.io 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 Qualified.io MCP in LangChain

Use `MultiServerMCPClient` to connect the server to your LangChain setup. You can write a chain that calls `list_assessments` to find the correct test ID, then feeds that ID directly into `invite_candidates` for the target candidate list.

Yes, every tool call like `get_assessment_result` or `invite_candidates_via_cohort` is fully tracked in LangSmith. You get complete visibility into latency, payload size, and LLM reasoning steps for every single candidate interaction.

Your LangChain agent can monitor test failures by calling `list_assessment_results` periodically. If a candidate ran into a technical glitch, the agent can automatically call `schedule_retry_assessment_result` to reopen the test.

You can write a simple maintenance chain. The agent checks for old tests using `list_assessments`, then calls `unpublish_assessment` and `archive_assessment` to remove them from your active pool.

All candidate code submissions and test scores pulled via `get_assessment_result_exhibit` run through Vinkius's secure V8 isolates. The raw candidate data is never stored on the platform, and your API tokens are kept isolated from the LLM context.

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