How to Use the AssessTEAM MCP in LangChain

Q: Can my LangChain agent create timesheets in AssessTEAM?

Yes. The agent uses the createtimesheetentry tool. You can build a chain that parses input from an email or chat and uses that data to populate the tool's arguments.

Q: What's the best way to pull project profitability from AssessTEAM with LangChain?

Use an agent equipped with the listprojects and getprofitabilityreport tools. The agent can find the right project first, then get the specific report, all in one sequence.

Build LangChain agents that run multi-step workflows with your AssessTEAM project and team data.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect AssessTEAM MCP to LangChain

Create your Vinkius account to connect AssessTEAM 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 AssessTEAM with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Project and Team Operations

The `list_projects` tool isn't just for fetching a list; it's the first link in a chain. Your LangChain agent can grab a project, then pipe its ID into `get_profitability_report` to check its financial health. It's about building sequences that mirror real work. You can design agents that `list_employees` on a team, then loop through them to pull individual `list_performance_evaluations`. This automates the prep work for team reviews, letting your agent handle the data gathering. This MCP server makes it all possible.

Automate Timesheets with LangChain Agents

Use the `create_timesheet_entry` tool as a final step in a custom workflow. An agent can parse a Slack message for project details and hours, then call the tool to log the time without anyone opening a new tab. It's a simple, powerful automation. You can make it smarter, too. Before creating an entry, have the agent call `list_timesheets` to check for duplicates from the same day. LangChain lets you build in these kinds of checks and balances.

Dynamic Reporting with this MCP Server

This is where you combine multiple tools for a complete picture. Your agent can start with `list_organizational_teams`, then find all `list_projects` associated with each team. From there, it can dig into the profitability of each project. The agent decides the path based on your goal. You don't have to hardcode the logic for every single step. Just give it the AssessTEAM tools and a target, and LangSmith will show you exactly how it got the answer.

Setup guide

Set up AssessTEAM 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 AssessTEAM 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({
    "assessteam-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 AssessTEAM 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 AssessTEAM. 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 AssessTEAM 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 AssessTEAM MCP in LangChain

You pass the tools from this MCP server to a LangChain agent. The agent's reasoning logic then decides to call `list_projects` and use its output to inform a subsequent call to `get_profitability_report`.

Yes. The agent uses the `create_timesheet_entry` tool. You can build a chain that parses input from an email or chat and uses that data to populate the tool's arguments.

Use an agent equipped with the `list_projects` and `get_profitability_report` tools. The agent can find the right project first, then get the specific report, all in one sequence.

Absolutely. Your agent can pull project data from the AssessTEAM MCP server, grab related files from a database, and then send a summary to Slack, all within the same chain.

Your agent only accesses the AssessTEAM data you grant it, like employee lists and performance evaluations. This MCP connection is secure, with all requests going through Vinkius's ephemeral sandboxes using your single endpoint token.

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