How to Use the Wallarm MCP in AutoGen

Q: How do I check the system inventory using AutoGen?

You can run getdiscoveredapiinventory and have one agent summarize the findings while another reviews the list against existing security policies, giving you a debated overview of your exposed APIs.

Q: Is Wallarm's MCP Server useful for general account checks?

The getclientinfo tool provides core client data. AutoGen can use this information to validate permissions before allowing agents to execute high-impact actions, like changing ACL rules.

Drive security consensus and debate with AutoGen and Wallarm's MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Wallarm MCP to AutoGen

Create your Vinkius account to connect Wallarm to AutoGen 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 Wallarm with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Debate vulnerability status

Assign one agent to report a finding using `search_vulnerabilities`, and another agent to challenge the severity. The third agent can then use `get_vulnerability_details` to pull deep diagnostic data before finally deciding on an update via `update_vulnerability_status`. This multi-agent debate structure ensures that vulnerability status changes are consensus-driven, not just automated.

Negotiate network access rules

You can set up agents to negotiate IP policy. One agent might detect an attack requiring a block (using `create_ip_acl_rule`), while another checks if the rule already exists using `list_ip_acl_rules`. The discussion converges on whether the change is necessary. This simulates real-world security operations where multiple teams must agree on policy changes.

Analyze blocked request payloads

When an attack hits, have a 'Forensics Agent' use `search_security_hits` to pull the raw payload. A separate 'Analysis Agent' can then process that data against known threat patterns. The debate concludes with a clear report of the malicious intent. This gives you highly detailed insight into failed attacks.

Setup guide

Set up Wallarm MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Wallarm tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Wallarm_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Wallarm data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Wallarm_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Wallarm data")
    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 Wallarm. 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 Wallarm 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 Wallarm MCP in AutoGen

AutoGen uses the tools to facilitate debate. For example, one agent might run `search_security_attacks`, and a second agent debates whether the resulting vector type requires an immediate IP block using `create_ip_acl_rule`.

Yes. You can design a debate where one agent identifies a false positive via `search_vulnerabilities`, and another agent confirms this by pulling details with `get_vulnerability_details` before issuing the command to update its status.

The server deals with API endpoints, request headers, and payloads when running tools like `search_security_hits`. When setting up agents, you must define strict roles for handling this sensitive information.

You can run `get_discovered_api_inventory` and have one agent summarize the findings while another reviews the list against existing security policies, giving you a debated overview of your exposed APIs.

The `get_client_info` tool provides core client data. AutoGen can use this information to validate permissions before allowing agents to execute high-impact actions, like changing ACL rules.

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