How to Use the Innform MCP in LangChain

Q: Can I filter training data by department in LangChain?

Yes, your agent can first call listdepartments to get the target group ID. Then, it uses that ID to filter the output of listlearners or cross-reference results.

Run multi-step training audits and compliance checks automatically inside your LangChain pipelines.

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Connect Innform MCP to LangChain

Create your Vinkius account to connect Innform 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 Innform with LangChain

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Claude

Perplexity

Map learner progress with LangChain chains

Stop manually cross-referencing completed courses against your employee roster. This MCP Server lets your agent run multi-step compliance checks. It queries `list_learners` to grab your active staff, then feeds those IDs directly into `list_results` to find out who completed their training. The output of one tool feeds directly into the next. Your agent handles the logic, figures out who is lagging behind, and flags the exact modules they missed by calling `list_modules` without you writing a single line of glue code.

Audit complex training paths automatically

Training paths are rarely simple, but your LangChain agent can now map them out instantly. By using `list_pathways` alongside `list_courses`, your pipeline inspects what curriculum is assigned to each department. If a team member changes roles, the agent runs `get_learner` to check their profile, compares it with the required courses, and identifies gaps. You get a clear picture of team readiness based on live data.

Trace every Innform MCP Server call in LangSmith

Debugging API calls in complex chains is usually a headache. With this integration, every single tool execution is fully visible inside your LangSmith dashboard. You see exactly when the agent calls `get_me` to verify permissions or pulls organizational structures via `list_locations`. If a query fails or hits a rate limit, you can pinpoint the exact payload. No more guessing why a learner search came up empty.

Setup guide

Set up Innform 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 Innform 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({
    "innform-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 Innform 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 Innform. 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 Innform MCP in LangChain

You install the LangChain MCP adapters package and initialize the multi-server client with the Vinkius endpoint. From there, call the tool retriever to pass `list_courses` and other endpoints directly to your agent.

No, this server is built for read-only tracking and auditing. Your agent can inspect records using `get_learner` or check assessments with `list_results`, but it cannot modify records or create new users.

Your chains should include backoff logic when pulling large datasets. If you run `list_learners` frequently across a massive team, cache the results locally to avoid hitting API thresholds.

Yes, your agent can first call `list_departments` to get the target group ID. Then, it uses that ID to filter the output of `list_learners` or cross-reference results.

Your API tokens never touch the LLM provider directly. Vinkius runs the server in an isolated sandbox, keeping your learner records and assessment results secure while your agent executes calls.

Use it with your favorite AI tools

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