How to Use the Height (Project Management) MCP in LangChain

Build ReAct agents in LangChain that query Height tasks and track project activities natively.

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Connect Height (Project Management) MCP to LangChain

Create your Vinkius account to connect Height (Project Management) 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 Height (Project Management) with LangChain

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Claude

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Read Height Data via MCP Server

The `list_tasks` and `get_task` tools pull project data straight into your LangChain pipelines. You give the agent a query about an ongoing sprint. It decides which endpoint to hit based on the intermediate context it gathered. Output from these tools feeds directly into your next chain link. If an agent finds a blocked ticket, it can pass that data to another tool to notify the team. LangSmith logs the exact token usage and latency for every API call.

Map Workspace Activity

The `list_activities` tool tracks exactly what happened across your Height workspace. Your LangChain agent can pull an audit log of recent changes before making a routing decision. This prevents the system from operating on outdated project state. You also get access to `workspace` and `list_lists`. These endpoints give the agent a structural map of where tickets live. It reads the setup once, then knows exactly how to navigate your specific project hierarchy.

Track Team Workloads

The `list_users` tool lets your ReAct agent check who is actually working on the project. When a manager asks for a capacity report, the agent cross-references users with their assigned tickets. It builds the response by chaining multiple read operations together. Everything happens autonomously. You define the prompt, and LangChain handles the tool execution loop. The agent stops querying only when it has enough data to answer the original request.

Setup guide

Set up Height (Project Management) 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 Height (Project Management) 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({
    "height-project-management-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 Height (Project Management) 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 Height. 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 Height (Project Management) MCP in LangChain

Use the MultiServerMCPClient class in your setup file. Pass your Height authentication details to the transport URL, then call get_tools() to bind them to your agent.

No, the current implementation is read-only. Your agent can fetch data using get_task or list_tasks, but it cannot create or modify records. You will need to handle mutations outside of this specific integration.

Polling large workspaces takes time. If you request all records via list_tasks without filters, the API response will be massive. Pass specific parameters to the tool calls to keep your agent fast.

Yes, you can initialize multiple MCP clients pointing to different workspace endpoints. LangChain aggregates the tools. Just ensure you give the agent clear instructions on which one to query.

Your ticket descriptions and activity logs flow directly from the server to your agent's memory. Vinkius runs the connection in an ephemeral V8 Isolate Sandbox. No task data is cached or stored after the chain completes execution.

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