How to Use the Lancerkit MCP in LangChain

Q: How do I connect Lancerkit to my LangChain agent via MCP?

Install langchain-mcp-adapters. Use MultiServerMCPClient pointing to your hosted URL. Call client.gettools() and hand them straight to your agent function.

Q: Can LangChain pipe Lancerkit time logs into external tools?

Yes. The agent pulls hours via gettimelogs. It then feeds those raw integers into any other tool in your LangChain environment.

Q: Does the Lancerkit MCP integration support LangSmith tracing?

It does. Every call to getinvoice or listservices logs latency and token usage in LangSmith. You see exactly what the agent did.

Q: What happens if an invoice query fails in LangChain?

The ReAct agent catches the error from getinvoice. It will automatically retry or switch to listinvoices to find the correct ID string.

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Connect Lancerkit MCP to LangChain

Create your Vinkius account to connect Lancerkit 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 Lancerkit with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

LangChain MCP Server Pipeline

Lancerkit exposes `list_tasks` and `get_time_logs` directly to your LangChain agents. You build a ReAct agent that pulls unbilled hours, feeds that exact integer into a pricing calculator chain, and drafts the total. No manual data entry required. The output of your time log query becomes the direct input for your invoice generator. If a client disputes a bill, your agent runs `get_invoice` to pull the specific string ID, checks the line items against `list_services`, and outputs a formatted breakdown. LangSmith tracks every token spent doing it.

Traceable Client Management

Grabbing client metadata starts with `list_clients` to find the target workspace, followed by `get_client` for the exact details. Your LangChain setup handles the routing. You write the prompt, and the agent decides the order of operations based on the intermediate steps. I used to spend hours digging through folders for project specs. Now, a simple chain fires `list_projects` and filters the active ones. You just pass `client.get_tools()` into your agent constructor and let it run.

Autonomous Billing Checks

Checking account health relies on `get_status` to verify the integration connection before any heavy lifting happens. Your agent checks this first. If the connection drops, the chain halts safely instead of throwing blind errors. Once verified, the agent groups data from `list_invoices` to map out your entire global pipeline statistics. You see exactly who owes you money. It runs headless in the background while you actually write code.

Setup guide

Set up Lancerkit 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 Lancerkit 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({
    "lancerkit-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 Lancerkit 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 Lancerkit. 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 Lancerkit MCP in LangChain

Install `langchain-mcp-adapters`. Use `MultiServerMCPClient` pointing to your hosted URL. Call `client.get_tools()` and hand them straight to your agent function.

Yes. The agent pulls hours via `get_time_logs`. It then feeds those raw integers into any other tool in your LangChain environment.

It does. Every call to `get_invoice` or `list_services` logs latency and token usage in LangSmith. You see exactly what the agent did.

The ReAct agent catches the error from `get_invoice`. It will automatically retry or switch to `list_invoices` to find the correct ID string.

This MCP Server reads your workspace client names and invoice string IDs. Vinkius runs the connection inside an ephemeral V8 Isolate Sandbox. The session dies the second your script finishes, leaving zero residual data behind.

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