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

Q: How do I ingest Levo.ai (API Security & Observability) data into LlamaIndex?

Use the llama-index-tools-mcp package to initialize the client. From there, wrap the MCP Server in an McpToolSpec to expose tools like listcatalogendpoints to your indexers.

Q: Does this MCP Server allow filtering of allowed tools in LlamaIndex?

Yes, you can restrict your agent to specific tools like exportopenapispec using the allowedtools filter during setup. This keeps your agent focused on documentation tasks without exposing raw vulnerability data.

Q: How do I retrieve endpoint details dynamically?

Your agent uses getendpointdetails to fetch deep schema structures on demand. LlamaIndex parses these JSON payloads and injects them directly into the agent's prompt context.

Index your live API schemas and vulnerability data directly into LlamaIndex for semantic, hallucination-free security queries.

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

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

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Index live API endpoints for semantic search in LlamaIndex

The `list_catalog_endpoints` tool retrieves active REST, GraphQL, and gRPC endpoints from your environments. LlamaIndex takes this raw catalog and indexes it into your vector store, turning live API metadata into a queryable knowledge base. Instead of guessing which service owns a route, you can search your index using natural language queries. The agent matches your intent to the actual schemas pulled by `get_endpoint_details`.

Query active vulnerabilities using grounded RAG

Use `list_vulnerabilities` to pull active security flaws directly into your LlamaIndex document store. This ground-truth data prevents your agent from inventing fake security issues during audits. When you ask about a specific risk, the MCP agent retrieves the exact exploitation evidence via `get_vulnerability`. You get precise, factual answers backed by live runtime telemetry.

Track sensitive data leaks using this MCP Server

The `list_sensitive_data` tool exposes endpoints that are actively transmitting regulated data like PII. Your index stores these findings, allowing you to run compliance checks against real-world traffic patterns. If an anomaly occurs, the agent calls `get_observation` to retrieve the exact runtime context. This lets you cross-reference historical data flows with fresh, live observations.

Setup guide

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

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Levo.ai (API Security & Observability) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Levo.ai (API Security & Observability) tools.",
)
response = await agent.run("List recent Levo.ai (API Security & Observability) data")

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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 LlamaIndex

Use the `llama-index-tools-mcp` package to initialize the client. From there, wrap the MCP Server in an `McpToolSpec` to expose tools like `list_catalog_endpoints` to your indexers.

Yes, you can build RAG pipelines that query live vulnerability data. The agent calls `list_vulnerabilities` to fetch real-time exposures and merges them with your static security policies.

Yes, you can restrict your agent to specific tools like `export_openapi_spec` using the allowed_tools filter during setup. This keeps your agent focused on documentation tasks without exposing raw vulnerability data.

Your agent uses `get_endpoint_details` to fetch deep schema structures on demand. LlamaIndex parses these JSON payloads and injects them directly into the agent's prompt context.

All API schemas and vulnerability logs remain contained within your private vector database. The MCP Server transmits this data through encrypted local channels, ensuring no external party can intercept your internal architecture details.

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