Can Levo.ai identify undocumented APIs in my environment?

Yes. Use the `list_catalog_endpoints` tool to see the full inventory auto-discovered by Levo sensors. Your agent can highlight 'Shadow' or 'Zombie' APIs that exist in your infrastructure but are missing from official documentation.

How do I check which endpoints are exposing PII or sensitive data?

The `list_sensitive_data` tool allows your agent to query endpoints categorized by regulated data flows. You'll see which paths are transmitting names, emails, SSNs, or financial data, helping you prioritize compliance audits.

Can my agent generate a live OpenAPI specification for an existing service?

Absolutely. Use the `export_openapi_spec` tool with a specific App ID. Your agent will retrieve a specification derived from actual observed traffic, providing a more accurate reflection of your live API than static files.

Levo.ai (API Security & Observability) MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Levo.ai (API Security & Observability)
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Levo.ai (API Security & Observability) through Vinkius, pass the Edge URL in the `mcps` parameter and every Levo.ai (API Security & Observability) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Levo.ai (API Security & Observability) Specialist",
    goal="Help users interact with Levo.ai (API Security & Observability) effectively",
    backstory=(
        "You are an expert at leveraging Levo.ai (API Security & Observability) tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Levo.ai (API Security & Observability) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Levo.ai (API Security & Observability) MCP Server

Connect your Levo.ai account to any AI agent and take full control of your API security posture and runtime observability through natural conversation.

When paired with CrewAI, Levo.ai (API Security & Observability) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Levo.ai (API Security & Observability) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Endpoint Orchestration — List all auto-discovered API endpoints (REST, GraphQL, gRPC) and identify undocumented shadow or zombie APIs directly from your agent
Sensitive Data Audit — Query categorizations for endpoints exposing regulated data flows including PII (names, emails), PHI (medical), and financial boundaries
Vulnerability Management — Monitor active API security vulnerabilities validating against OWASP boundaries, including BOLA instances and broken authentication
OpenAPI Generation — Export live, precisely accurate OpenAPI specifications derived immediately from actual observed traffic rather than static manual definitions
Behavioral Monitoring — Analyze runtime API traffic patterns and anomalous observations detected by live sensors indicating unexpected schema drift
Diagnostic Investigation — Retrieve detailed diagnostic exploitation evidence for specific vulnerabilities to understand root causes and remediation steps

The Levo.ai (API Security & Observability) MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Levo.ai (API Security & Observability) to CrewAI via MCP

Follow these steps to integrate the Levo.ai (API Security & Observability) MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Levo.ai (API Security & Observability)

Why Use CrewAI with the Levo.ai (API Security & Observability) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Levo.ai (API Security & Observability) through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Levo.ai (API Security & Observability) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Levo.ai (API Security & Observability) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Levo.ai (API Security & Observability) for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Levo.ai (API Security & Observability), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Levo.ai (API Security & Observability) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Levo.ai (API Security & Observability) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Levo.ai (API Security & Observability) MCP Tools for CrewAI (10)

These 10 tools become available when you connect Levo.ai (API Security & Observability) to CrewAI via MCP:

export_openapi_spec

Export a live auto-generated OpenAPI payload specification for an application

get_endpoint_details

Get deep detailed schema structure for a specific discovered API endpoint

get_observation

Get details of a specific runtime anomalous observation

get_vulnerability

Get detailed diagnostic exploitation evidence for a specific API vulnerability

list_applications

List all API applications (services) tracked by Levo.ai

list_catalog_endpoints

Identifies REST, GraphQL, gRPC, and SOAP endpoints — including undocumented shadow and zombie APIs mapped dynamically. List all discovered API endpoints in the Levo.ai catalog

list_environments

List deployment boundaries environments monitored by Levo active sensors

list_observations

List runtime API behavior observations detected by Levo sensors

list_sensitive_data

List categorized API endpoints exposing sensitive or regulated data flows

list_vulnerabilities

List active API security vulnerabilities discovered across all applications

Example Prompts for Levo.ai (API Security & Observability) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Levo.ai (API Security & Observability) immediately.

"List all discovered API endpoints in our Levo catalog"

"Show me the active OWASP vulnerabilities for the 'Checkout' application"

"Generate a live OpenAPI spec for the 'User Management' service"

Troubleshooting Levo.ai (API Security & Observability) MCP Server with CrewAI

Common issues when connecting Levo.ai (API Security & Observability) to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Levo.ai (API Security & Observability) + CrewAI FAQ

Common questions about integrating Levo.ai (API Security & Observability) MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.