MCP Discovery

MCP servers are easy to stand up and hard to track. They appear in developer environments, cloud projects, SaaS integrations, and inside agent runtimes, often without security or platform team visibility. MCP Discovery turns that sprawl into a known surface that can be inventoried, scored, and governed before agents call it.

MCP Discovery typically covers:

• Cloud platform discovery: Surfacing MCP servers running on AWS, Azure, GCP, Databricks, and Copilot Studio.
• Code repository scanning: Detecting MCP server definitions and client configurations in GitHub and CI pipelines.
• Runtime telemetry: OpenTelemetry-based identification of live MCP traffic between agents and tools.
• Tool capability mapping: Cataloging which tools each server exposes and which data sources they reach.
• Risk and provenance scoring: Tagging each discovered server with vulnerability, publisher trust, and license context.

MCP Discovery is distinct from an MCP Registry: discovery is the act of finding what exists, while a registry is the curated, policy-governed catalog of what is allowed. Both are required for safe MCP adoption at enterprise scale, and discovery is the input that keeps the registry honest.

How PointGuard AI Helps

PointGuard's AI Discovery continuously identifies MCP servers and tools across cloud platforms, code repositories, and runtime telemetry, while the MCP Security Gateway turns discovery findings into approval workflows, risk-scored entries in the Trusted MCP Directory, and per-tool policy enforced at runtime.

Learn More

OWASP Top 10 for Agentic Applications

Wikipedia: Model Context Protocol

The Register: Bug Hunter Tracks Down Three Serious MCP Database Flaws