How to Use the Harness MCP in LangChain

Q: How do I connect LangChain to the Harness MCP Server?

Install langchain-mcp-adapters and langgraph via pip. Initialize a MultiServerMCPClient pointing to the endpoint, then pass client.gettools() to your agent.

Q: Can LangChain trigger a Harness pipeline automatically?

Yes. The agent uses the executepipeline tool to start builds based on your chain's logic. It handles the parameters required by the target environment.

Q: How does LangChain monitor Harness execution status?

Your script loops the getexecutionstatus tool. The ReAct agent reads the step details and decides whether to report success or alert you about a failure.

Build multi-step deployment chains with LangChain and this MCP Server that trigger builds, check status, and verify logs automatically.

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Connect Harness MCP to LangChain

Create your Vinkius account to connect Harness 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 Harness with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Harness pipeline execution with LangChain

Your LangChain agent starts by mapping out the environment using `list_projects` and `list_pipelines`. It feeds those project IDs directly into the next step of your ReAct loop. You don't have to hardcode anything. The agent reads the available configurations and decides exactly which deployment target matches the user's prompt.

Trigger and trace deployments

Calling `execute_pipeline` kicks off the actual build process inside your infrastructure. The agent then polls `get_execution_status` to monitor the steps as they run. Because you are using LangSmith, every API request and response is fully traced. You see the exact token usage and latency for every deployment check.

Automated compliance checks via MCP Server

The MCP Server exposes `get_audit_logs` so your agent can pull historical actions before making changes. It cross-references these logs against the current configuration. If an engineer asks who touched a specific environment, the agent checks `list_secrets` and the audit trail to build a complete picture of recent modifications.

Setup guide

Set up Harness 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 Harness 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({
    "harness-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 Harness transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Harness MCP in LangChain

Install `langchain-mcp-adapters` and `langgraph` via pip. Initialize a `MultiServerMCPClient` pointing to the endpoint, then pass `client.get_tools()` to your agent.

Yes. The agent uses the `execute_pipeline` tool to start builds based on your chain's logic. It handles the parameters required by the target environment.

Your script loops the `get_execution_status` tool. The ReAct agent reads the step details and decides whether to report success or alert you about a failure.

The agent catches the failed state from the API response. It then pulls `get_pipeline` to read the YAML and suggest a fix.

The V8 Isolate Sandbox drops the connection immediately after execution. Your pipeline YAML, environment variables, and `list_secrets` metadata stay completely ephemeral during the session.

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