Is the integration read-only?

Yes. All tools only read from your Stoplight workspace — projects, nodes, schemas, and activity logs. No modifications or deletions are possible through this integration.

What is the workspace slug?

It's the subdomain of your Stoplight URL. For example, if your workspace is at `https://acme.stoplight.io`, the slug is `acme`. You can find it in the address bar when logged in.

What data can the agent access?

Projects, workspace members, activity logs, and all project nodes (endpoints, models, articles). Each node's full content — including OpenAPI schemas — can be retrieved via `get_node_details`.

Stoplight MCP Server for LangChain 7 tools — connect in under 2 minutes

Name: Stoplight
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Stoplight through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "stoplight": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Stoplight, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Stoplight MCP Server

Integrate the industry-leading API design and documentation capabilities of Stoplight into your conversational AI workflows. Empower your engineering teams to explore workspaces, evaluate OpenAPI schemas, and audit API projects natively from their conversational assistant. Securely map your AI to your Stoplight workspace, enabling the orchestration of complex documentation tasks, project navigation, and architectural reviews naturally without switching contexts or opening complex dashboards.

LangChain's ecosystem of 500+ components combines seamlessly with Stoplight through native MCP adapters. Connect 7 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Workspace Exploration — Rapidly inspect top-level organizational containers invoking list_workspaces, and track operational changes programmatically leveraging list_workspace_activity.
Project Management — Audit your API documentation repositories cataloging initiatives securely using list_projects, and retrieve full visibility metadata invoking get_project_details.
Schema & Documentation Discovery — Dive deeply into specific documentation structures retrieving files, endpoints, and models leveraging list_project_nodes, and parse their raw text safely utilizing get_node_details.
Team & Governance — Map project ownership accurately and enforce governance metrics iteratively assigning roles retrieving authorized contributors naturally via list_workspace_members.

The Stoplight MCP Server exposes 7 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Stoplight to LangChain via MCP

Follow these steps to integrate the Stoplight MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 7 tools from Stoplight via MCP

Why Use LangChain with the Stoplight MCP Server

LangChain provides unique advantages when paired with Stoplight through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Stoplight MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Stoplight queries for multi-turn workflows

Stoplight + LangChain Use Cases

Practical scenarios where LangChain combined with the Stoplight MCP Server delivers measurable value.

RAG with live data: combine Stoplight tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Stoplight, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Stoplight tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Stoplight tool call, measure latency, and optimize your agent's performance

Stoplight MCP Tools for LangChain (7)

These 7 tools become available when you connect Stoplight to LangChain via MCP:

get_node_details

Retrieves details for a specific documentation node

get_project_details

Retrieves details for a specific Stoplight project

list_project_nodes

Lists all documentation nodes (files, endpoints, models) within a project

list_projects

Lists all projects in a specific Stoplight workspace

list_workspace_activity

Lists recent activity logs for a Stoplight workspace

list_workspace_members

Lists all members of a Stoplight workspace

list_workspaces

Lists all accessible Stoplight workspaces

Example Prompts for Stoplight in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Stoplight immediately.

"List my Stoplight projects and show recent workspace activity."

"Retrieve the detailed schema documentation for the processing node in our core billing API project."

"List all active members in the current workspace."

Troubleshooting Stoplight MCP Server with LangChain

Common issues when connecting Stoplight to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Stoplight + LangChain FAQ

Common questions about integrating Stoplight MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.