How to Use the Leiga MCP in LangChain

Q: How do I connect the Leiga MCP Server to my LangChain agent?

Install the adapter package using pip install langchain-mcp-adapters langgraph. Initialize the MultiServerMCPClient with the Vinkius endpoint, get the tools, and pass them to your agent constructor.

Run multi-step project management chains using the Leiga MCP Server and LangChain to plan sprints and assign tasks.

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Connect Leiga MCP to LangChain

Create your Vinkius account to connect Leiga 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 Leiga with LangChain

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Claude

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Chained Sprint Planning with LangChain and Leiga

The Leiga MCP Server exposes project management tools directly to your LangChain agents so they can run sequential planning workflows. Your agent starts by calling `list_projects` to find the active board, feeds that output into `list_tasks` to identify open tickets, and finishes by running `list_team_members` to check who has the bandwidth for new work. Because LangChain handles sequential tool calls out of the box, you don't have to write glue code to pass IDs between steps. The output of your backlog search automatically becomes the input for task assignment, keeping your team moving without manual copy-pasting.

Trace Task Actions with LangSmith and Leiga

This Leiga MCP Server integration lets you monitor every project update your agent makes using LangSmith tracing. When an agent calls `create_task` to spin up a new ticket, you can see the exact prompt, the tool inputs, and the API response time in your LangSmith dashboard. If an automated transition fails during a complex workflow, you won't have to guess what went wrong. You can pinpoint exactly why `get_task_details` returned a specific status or where the chain broke during a team capacity check.

Automated Workload Balancing Pipelines

Run autonomous loops that compare current workloads against active workflows using Leiga tools. The agent queries your setup with `list_workflows`, pulls team assignments with `list_organizational_teams`, and evaluates who is drowning in work before assigning new tickets. By combining these tools with LangChain's 500+ integrations, you can pull developer activity from other platforms and match it against Leiga task data. Your agent makes decisions based on actual capacity, not outdated spreadsheets.

Setup guide

Set up Leiga 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 Leiga 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({
    "leiga-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 Leiga 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 Leiga. 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 Leiga MCP in LangChain

Install the adapter package using `pip install langchain-mcp-adapters langgraph`. Initialize the `MultiServerMCPClient` with the Vinkius endpoint, get the tools, and pass them to your agent constructor.

Yes, LangChain agents use the ReAct framework to chain tool executions. The agent can call `list_tasks` to find blocked items and immediately trigger `get_task_details` to analyze the roadblocks.

LangSmith tracks every tool call made by the agent in real time. You can view the inputs and outputs of `create_task` or check latency on `list_projects` to optimize your pipelines.

Yes, you can filter the tool list returned by the client before passing them to your agent. If you only want the agent to read data, simply omit `create_task` from the tools array.

Your task descriptions, team rosters, and project details are never stored or used to train public models. Vinkius runs the server in an isolated sandbox and handles authentication securely.

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