Levo’s MCP Server for Safe, Scalable Agentic Productivity

Buchi Reddy B

CEO and Founder at Levo

The Levo team and I are proud to launch the first-of-its-kind API Security Model Context Protocol (MCP) Server that further advances our mission of helping enterprises accelerate, maximize, and retain API-led revenue and while releasing secure applications faster. 

Levo’s MCP Server enables faster, more secure, and universal access to real-time, context-rich access to security intelligence for DevSecOps teams and AI agents without breaking workflows or compromising safety.

This standardized access means it exposes secure, scoped, and structured access to Levo’s security data lake, including API docs, endpoint catalog, runtime traces, API vulnerability information, exploit data, and authentication states, without requiring users to log in to the portal, navigate dashboards, or request access tokens.

But this isn’t just another data access layer. 

Because the MCP Server is built directly atop Levo’s proven primitives, our continuous API Visibility and Context-Aware Security Testing modules, your teams are enabled with real insights, not just data.

At the core of this is what Levo continuously constructs: a living, evolving enterprise-specific knowledge graph that understands your infrastructure, APIs, sensitive data flows, and access patterns across environments.

This is what makes the MCP Server so powerful—it surfaces that knowledge graph securely and scalably, giving developers, security teams, and AI agents the context they need to act, automate, and accelerate.

In doing so, Levo’s MCP Server turns innovation cycles from months into days through faster, smarter, and more productive Dev Sprints, and with AI-based vulnerability triaging and remediation. 

The sections that follow detail the specific coordination and visibility challenges introduced by AI agent adoption.

Levo’s MCP Server Turns AI Agents Into Operators

AI agents have already delivered impressive productivity gains across enterprise workflows. 

Today, most enterprise agents still operate without direct visibility into runtime behavior, test results, or authentication posture. 

Instead, they rely on static documentation, disconnected corpora, or public information not tailored to the enterprise—limiting their effectiveness in workflows where correctness is non-negotiable.

It’s not an adoption problem—it’s an infrastructure one.

92.7% of executives cite data security and governance as the primary barrier to successful AI implementation. 37% report major integration challenges due to fragmented IT ecosystems. And 42% say it takes eight or more integrations just to operationalize an agent’s output.

Without standardized interfaces, teams resort to brittle workarounds—wrapping APIs, hardcoding scripts, and stitching together plugins. Each new agent adds more complexity, more blind spots, and more context fragmentation.

And when agents can’t access live traces, understand payload behavior, or validate test outcomes, they stay stuck in read-only mode.

They can suggest but not act. Surface but not triage. Detect but not remediate.

Levo’s MCP Server was built to close this loop, turning passive agents into active operators. It provides the governed, real-time access layer that lets AI systems evolve from helpful sidekicks into trusted automation engines.

Levo’s MCP Server ensures Agentic Support is a Force Multiplier Not a Security Risk

Gartner notes that AI success depends on access to well-structured, governed data—yet most enterprises lack the infrastructure to make that possible.

Levo’s MCP Server fills this gap by turning fragmented runtime signals into secure, structured insights without requiring new pipelines or exposing raw data.

At the foundation is Levo’s eBPF-based Sensor, which continuously monitors live API behavior in production. It captures authentication logic, data exposure, request/response traces, and more.

This raw telemetry is then enriched by Levo’s Satellite engine, which deduplicates, correlates, and converts it into standardized formats, making it readable and reliable for both humans and LLMs.

Most enterprises struggle with this normalization layer, as highlighted by Deloitte. 

Levo removes that burden entirely, handling data capture, processing, and policy enforcement at the platform level.

The MCP Server then exposes this processed intelligence via two programmable primitives:

Where other vendors retrofit agents onto brittle foundations, limiting them to passive observation, Levo solved the architectural problem first.

As a result, our upcoming AI Security Agents will be the first capable of automating workflows securely, contextually, and at scale.

From Friction to Flow: What MCP Unlocks for Teams Today

Levo’s deeply integrated, context-aware AI Agents are just a few weeks away—but even today, exposing the MCP Server to your DevSecOps teams and existing agents dramatically boosts velocity, throughput, and security posture without growing your team.

Let’s see through a few live examples how they can speed up workflows: 

Security Posture That’s Not Just Strong, It’s Searchable

Levo already continuously tests each API using 1000+ exploit-aware payloads to catch AuthZ gaps, injection flaws, and logic-layer abuse before they reach production. Every test is validated in real context and linked back to the originating API, environment, and responsible service.

But even with this level of depth and coverage, most security teams are still left navigating dashboards to answer a simple question: “What’s been tested, and what hasn’t?”

With Levo’s MCP Server, they no longer have to guess, dig, or delegate. Now, security engineers or the copilots they work with can query Levo’s API Security Data Lake directly from Claude or ChatGPT and instantly retrieve live test coverage across any application.

Within seconds, they can:

The result isn’t just awareness. 

It’s clarity with full traceability. 

Every test result is scoped, source-linked, and backed by real-world evidence, so engineers understand not just what’s tested, but why it matters.

This transforms security planning from guesswork into a governed, queryable workflow:

In an enterprise environment where audit cycles, attack windows, and release schedules often overlap, this capability is a multiplier. 

It ensures testing coverage isn’t just broad or deep it’s also transparent, answerable, and always in reach.

Full Coverage Is the Starting Point. Custom Testing Is a Query Away

Legacy security testing tools still work like scanners—they run predefined checks, deliver vague verdicts, and leave teams guessing what actually happened. 

Levo replaced that model with real-world testing: replayable payloads, trace-linked results, and context-aware attack paths built on real API behavior.

But now, with MCP, that precision becomes a creative input not just a fixed output.

Security engineers and copilots can pull the trace for any API right from the MCP Server and immediately convert it into a curl command representing the real user interaction, complete with headers, tokens, and parameter values.

From there, AI agents inside tools like Cursor or Claude don’t just read the command they can mutate it:

Each test is rooted in an actual request that hits your system, not a synthetic guess. And because this happens inside IDEs or AI terminals, security teams can:

This means the time between a “suspicious trace” and a “confirmed exploit” collapses from hours to moments:

Testing isn’t just continuous now it’s composable. Your traces become launchpads. Your agents become researchers. 

And your API security posture doesn’t just improve, it evolves in sync with how attackers think.

Book a demo through this link to see this live!