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

Enforce strict type-safe validation on API vulnerability audits using Pydantic AI and this dedicated MCP server.

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

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

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Validate endpoint schemas using Pydantic AI

`get_endpoint_details` returns the deep schema structure for a specific discovered API endpoint. Your Pydantic AI agent parses this structure against strict runtime models to guarantee that no unexpected parameters slip through. The agent uses `list_catalog_endpoints` to discover REST, GraphQL, or gRPC routes across your network. Because every response is validated, your application fails loudly if a schema change violates your defined types.

Audit vulnerabilities with strict type safety

`list_vulnerabilities` lists active API security vulnerabilities across your applications. Pydantic AI maps this payload directly to typed Python classes, preventing your agent from processing corrupted vulnerability data. It calls `get_vulnerability` to retrieve the exact exploitation evidence. You get structured, type-safe security reports that your development pipelines can parse without throwing parsing errors.

Track sensitive data via the MCP Server

`list_sensitive_data` identifies endpoints that expose sensitive data flows like PII or PHI. Your agent monitors these routes to verify that they conform to your internal compliance schemas. It matches this with `list_environments` to ensure sensitive data rules are applied differently in staging versus production. This prevents real customer data from leaking into test systems.

Setup guide

Set up Levo.ai (API Security & Observability) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "levoai-api-security-observability-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Levo.ai (API Security & Observability) tools.",
)

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

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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 Pydantic AI

You register the MCP Server using the MCPToolset constructor with your server URL. The agent then dynamically accesses tools like list_applications while enforcing strict runtime validation.

The framework raises a validation error immediately. This prevents the agent from hallucinating security fixes based on malformed API data.

Yes, the framework natively handles asynchronous tool calls. Tools like list_observations execute in the background without blocking your main application loop.

Yes, the server must run externally. You pass the running server's HTTP or SSE endpoint directly to your toolset configuration.

All data passing through the server remains encrypted in transit. Vinkius executes the MCP Server in isolated V8 sandboxes, ensuring that raw sensitive schemas are never cached or exposed.

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