How to Use the Beagle Security MCP in LangChain

Q: How do I start a Beagle Security scan using a LangChain agent?

Your agent can call the starttest tool with the ID of the application you want to scan. First, you'll probably want the agent to use listapplications to get a correct list of targets to choose from.

Q: Can a LangChain agent read Beagle Security test results?

Yes. Once a test is complete, the agent calls the gettestresult tool using the test session ID. It gets back a full JSON report of all findings, which you can then parse and use in the next step of your chain.

Q: Can I manage multiple applications at once?

Absolutely. The listapplications and listprojects tools give your agent a full view of your environment. You can design your agent to iterate through all applications and run tests on each one sequentially or in parallel.

Build autonomous security audit pipelines for any of your applications using LangChain and the Beagle Security tools.

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Create your Vinkius account to connect Beagle Security 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 Beagle Security with LangChain

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Automate Your Security Scans

This toolset lets your LangChain agent manage security tests from start to finish. Your agent can `list_projects` and `list_applications` to see what's available, then use that info to kick off a targeted scan with `start_test`. No manual steps needed. Once a test is running, the agent can poll its progress using `get_test_status`. When the scan finishes, it pulls the complete findings with `get_test_result`. You can chain these steps into a single, automated workflow that runs on a schedule or a trigger, like a new code commit.

Build Smarter Security Logic

Go beyond simple start-and-stop scripts. With LangChain, you can build agents that make decisions based on test outcomes. For example, an agent can parse the JSON from `get_test_result` and, if it finds critical vulnerabilities, automatically create a ticket in another system using a different MCP Server. This MCP Server gives your chains the raw data they need to act. You can design a process where an agent checks all `get_running_tests`, finds one that's stalled, and uses `stop_test` before restarting it. It's about building resilient, intelligent security automation, not just brittle scripts.

Full Observability in LangChain

Every call to the Beagle Security tools is just another link in your chain. That means you get complete visibility through tools like LangSmith. You can trace exactly when your agent decided to `start_test` and what parameters it used. Debugging is straightforward. If a test fails to start, you can see the exact inputs and outputs from the MCP tool call. This makes it easier to figure out if the problem is in your agent's logic or the security testing environment itself.

Setup guide

Set up Beagle Security 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 Beagle Security 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({
    "beagle-security-alternative-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 Beagle Security transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Beagle Security MCP in LangChain

Your agent can call the `start_test` tool with the ID of the application you want to scan. First, you'll probably want the agent to use `list_applications` to get a correct list of targets to choose from.

Yes. Once a test is complete, the agent calls the `get_test_result` tool using the test session ID. It gets back a full JSON report of all findings, which you can then parse and use in the next step of your chain.

The best way is to create a loop in your agent's logic that calls `get_test_status` periodically. The agent can wait for the status to change to 'Completed' before moving on to fetch the results.

Absolutely. The `list_applications` and `list_projects` tools give your agent a full view of your environment. You can design your agent to iterate through all applications and run tests on each one sequentially or in parallel.

This server only processes metadata about your applications and the results of your security tests. All requests are handled in an ephemeral, zero-trust sandbox. We don't store your test results or application details after the API call is complete.

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