Enables secure interoperability between LLM agents and MCP tool servers by sanitizing requests and responses, masking sensitive tokens, detecting PII, and performing server reputation scans.
Enables AI agents to securely invoke tools by enforcing identity proof, capability verification, and risk scoring on every request, blocking unsafe calls before they execute.
Validates agent outputs in multi-agent systems to prevent coordination failures, with tools for schema verification, hallucination detection, and freshness checks, all with zero LLM cost.
Fact-checks and fixes AI outputs by catching hallucinations, repairing broken JSON, and correcting errors before they reach users, with tools for verification, validation, and correction.
Exposes LLM output validation (schema compliance and prompt-injection detection) as an MCP tool, enabling any MCP-compatible agent to apply real-time guardrails without importing the codebase.
Protects AI agents from prompt injection attacks, jailbreak attempts, and common web vulnerabilities by screening untrusted input through semantic LLM analysis and static pattern matching.