How to Use the Levo.ai (API Security & Observability) MCP in LangChain

Q: How do I connect the Levo.ai (API Security & Observability) MCP Server to my LangChain agents?

Install the langchain-mcp-adapters package and configure the server URL. Once connected, call client.gettools() to load the API discovery and vulnerability tools directly into your agent's toolkit.

Q: Can LangChain run multi-step audits using Levo.ai (API Security & Observability)?

Yes, you can build chains where the agent first calls listcatalogendpoints and then feeds those results into getendpointdetails. This allows the agent to build a detailed security profile for any newly discovered route on the fly.

Q: Can I generate OpenAPI specs dynamically using this integration?

Yes, your agent can call exportopenapispec to pull live, auto-generated specs for any tracked application. This lets you compare live traffic schemas against static repository code.

Chain Levo.ai tools together in LangChain to auto-discover shadow endpoints and pull raw vulnerability evidence in real time.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Levo.ai (API Security & Observability) MCP to LangChain

Create your Vinkius account to connect Levo.ai (API Security & Observability) 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.

GDPR Free for Subscribers

Setup Levo.ai (API Security & Observability) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Audit shadow APIs using this security MCP Server

Run the `list_catalog_endpoints` tool to find undocumented, zombie, or shadow API routes active in your staging and production environments. Your agent takes this raw list and compares it directly with your static schema files to catch undocumented changes. If a weird route pops up, the chain triggers `get_endpoint_details` to inspect the exact payload structure. You get immediate visibility into unmapped parameters without wastefully digging through gigabytes of raw logs.

Trace and isolate active OWASP vulnerabilities

The `list_vulnerabilities` tool pulls active security issues directly into your LangChain reasoning loops. Your agent inspects these flaws, maps them to known OWASP categories, and flags critical exposures instantly. For deeper triage, the agent calls `get_vulnerability` to extract the exact exploitation evidence and payload details. This gives developers the exact data they need to patch the flaw without back-and-forth Slack debates.

Monitor sensitive data flows inside LangChain pipelines

Use `list_sensitive_data` to track where PII or PHI is leaking across your microservices. Your agent maps these data flows dynamically, flagging unauthorized endpoints that expose regulated information. By pairing this MCP tool with `list_environments`, you can isolate these checks to staging or production zones. This keeps your compliance audits accurate and fast, saving hours of manual security reviews.

Setup guide

Set up Levo.ai (API Security & Observability) 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 Levo.ai (API Security & Observability) 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({
    "levoai-api-security-observability-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 Levo.ai (API Security & Observability) transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Levo.ai. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Levo.ai (API Security & Observability) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Levo.ai (API Security & Observability) MCP in LangChain

Install the `langchain-mcp-adapters` package and configure the server URL. Once connected, call `client.get_tools()` to load the API discovery and vulnerability tools directly into your agent's toolkit.

Yes, you can build chains where the agent first calls `list_catalog_endpoints` and then feeds those results into `get_endpoint_details`. This allows the agent to build a detailed security profile for any newly discovered route on the fly.

Yes, execution is fully tracked if you use LangSmith tracing. You can view latency and inputs for tools like `list_vulnerabilities` or `get_observation` directly in your dashboard.

Yes, your agent can call `export_openapi_spec` to pull live, auto-generated specs for any tracked application. This lets you compare live traffic schemas against static repository code.

The MCP Server runs inside a zero-trust V8 sandbox. Raw payload content never leaves your local environment, keeping your PII and PHI flows isolated and secure.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript