How to Use the Coassemble MCP in LangChain

Q: Can LangChain automate student grading audits with Coassemble?

Yes. You can build a chain that runs gettrainingcompletionresults to find completed courses and updates your internal databases with the scores.

Q: How does LangSmith trace Coassemble MCP Server tool calls?

LangSmith monitors every execution of enrollmemberincourse or other tools. It logs latency, token usage, and payload data so you can debug failed enrollments easily.

Run your LMS directly from LangChain chains to enroll users and track training results dynamically.

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Connect Coassemble MCP to LangChain

Create your Vinkius account to connect Coassemble 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 Coassemble with LangChain

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Automate LMS administration with this MCP Server

The `list_coassemble_members` tool lets your agent pull your student directory straight into any LangChain execution chain. From there, the agent can inspect active users and run `enroll_member_in_course` to assign training modules without manual intervention. This setup removes the friction of jumping between your LMS dashboard and your database. By piping these tools directly into your ReAct agent, you build self-correcting enrollment pipelines that run on autopilot.

Track student progress inside your LangChain pipelines

The `get_training_completion_results` tool fetches real-time grades and course progress for any active enrollment. Your LangChain agent can query this data to trigger follow-up emails, update external CRMs, or flag struggling students. Instead of waiting for weekly CSV exports, you get live status checks. This lets your agent make decisions based on actual student performance metrics, keeping your records updated automatically.

Group management via LangChain agent actions

The `get_member_group_associations` tool queries which student groups a specific member belongs to inside your workspace. Your agent can cross-reference this with `list_coassemble_groups` to make sure cohorts stay organized. You build clean, multi-step reasoning chains that audit your entire user list. If a student is missing from a cohort, the agent spots the gap and corrects it instantly.

Setup guide

Set up Coassemble 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 Coassemble 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({
    "coassemble-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 Coassemble 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 Coassemble. 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 Coassemble MCP in LangChain

You install the MCP adapter package and pass the Vinkius server URL to your multi-server client. The client fetches tools like `list_coassemble_courses` and exposes them directly to your agent as runnable tools.

Yes. You can build a chain that runs `get_training_completion_results` to find completed courses and updates your internal databases with the scores.

LangSmith monitors every execution of `enroll_member_in_course` or other tools. It logs latency, token usage, and payload data so you can debug failed enrollments easily.

You can restrict which tools the agent accesses during initialization. If you only want the agent to read data, just expose `list_coassemble_courses` and omit the enrollment tools.

Your student records, grades, and course data are isolated inside a secure V8 sandbox. Vinkius handles the underlying API credentials, meaning your active MCP server is never exposed to raw tokens.

Use it with your favorite AI tools

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