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

Q: How do I register the Levo.ai (API Security & Observability) MCP Server in AutoGen?

Install autogen-extmcp and use mcpservertools to fetch the tool definitions. You can then pass this tool list directly to your AssistantAgent constructor.

Q: Can I target specific environments in my multi-agent workflows?

Yes, agents can call listenvironments to discover active deployment boundaries. This allows them to filter vulnerabilities and endpoint catalogs to only run checks on staging or production.

Deploy AutoGen agents to debate API security risks, verify schemas, and triage vulnerabilities using live Levo.ai telemetry.

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

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

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

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Debate API security postures using this MCP Server

The `list_vulnerabilities` tool feeds active security issues directly into your multi-agent conversation. A security agent can argue for immediate patching while a developer agent uses `get_vulnerability` to review the actual exploit evidence. This collaborative debate ensures you only prioritize real, verifiable risks. By forcing agents to negotiate, you eliminate the alert fatigue caused by standard automated scanners.

Validate live schemas against spec files in AutoGen

Run `export_openapi_spec` to let your agents generate live OpenAPI specs from real traffic. A QA agent can then compare this live spec against the repository version to find undocumented changes. If discrepancies exist, the agent triggers `list_catalog_endpoints` to pinpoint the exact routes causing the drift. You get automated drift detection without writing manual test scripts.

Monitor live anomalies and data exposure zones

Use `list_observations` to feed runtime anomalies straight to your monitoring agent. This agent tracks weird behaviors and coordinates with other agents to investigate the root cause. When sensitive data is involved, the MCP agent calls `list_sensitive_data` to verify if PII is leaking. This lets your team build autonomous compliance guardrails that react the second a policy is breached.

Setup guide

Set up Levo.ai (API Security & Observability) 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 Levo.ai (API Security & Observability) 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="Levo.ai (API Security & Observability)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Levo.ai (API Security & Observability) 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="Levo.ai (API Security & Observability)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Levo.ai (API Security & Observability) 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 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.

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

Install `autogen-ext[mcp]` and use `mcp_server_tools` to fetch the tool definitions. You can then pass this tool list directly to your `AssistantAgent` constructor.

Yes, the tools are stateless and support concurrent access. Your security agent can fetch active flaws with `list_vulnerabilities` while your developer agent pulls schema details via `get_endpoint_details`.

Yes, the MCP adapter automatically translates the server's tool schemas into the format required by your agents. This means you do not have to write custom wrappers for tools like `list_catalog_endpoints`.

Yes, agents can call `list_environments` to discover active deployment boundaries. This allows them to filter vulnerabilities and endpoint catalogs to only run checks on staging or production.

The MCP Server runs inside an ephemeral, zero-trust sandbox that isolates all endpoint details and security logs. No data is stored permanently on the hosting platform, keeping your architecture layout completely confidential.

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