Shield MCP

Shield MCP is a lightweight security middleware designed to protect Model Context Protocol (MCP) servers without requiring changes to the official MCP SDK. By inserting a single decorator around existing tool functions, developers can enforce strict access controls, sanitize sensitive data, log every interaction in a structured format, and throttle requests—all while keeping the underlying MCP implementation untouched. This approach allows teams to add robust security layers to AI assistants that call external tools, ensuring compliance with internal policies and reducing the risk of accidental data leaks.

The core value proposition lies in its zero‑touch integration. Developers simply wrap any MCP tool with , specifying a whitelist of permissible tools, optional sanitization logic, and rate‑limit parameters. The middleware automatically validates tool names against the whitelist, applies configurable text sanitizers to filter out credit card numbers or email addresses, and enforces a token‑bucket rate limit that can be tuned per user or session. Because all audit events are emitted through , logs are machine‑readable and can be fed into SIEMs, observability platforms, or custom dashboards for real‑time monitoring and forensic analysis.

Key capabilities include:

• Tool Access Control – A declarative whitelist that blocks unauthorized tool calls before they reach the backend, preventing privilege escalation or accidental execution of sensitive operations.
• Result Sanitization – Customizable sanitizers that trim output length, redact patterns, or transform data to meet privacy regulations.
• Structured Logging – Consistent audit logs capturing tool name, arguments, user and session identifiers, timestamps, and execution status, facilitating compliance audits and debugging.
• Rate Limiting – A token‑bucket algorithm that limits requests per minute with burst tolerance, protecting backend services from overload and abuse.
• Error Handling – Unified error formatting that hides implementation details while providing actionable messages to the client.

In practice, Shield MCP shines in scenarios where AI assistants interact with critical infrastructure or sensitive data sources. For example, a customer‑support chatbot that queries internal knowledge bases can restrict its toolset to read‑only operations, sanitize any returned personal information, and log each query for compliance audits. Similarly, a data‑analysis assistant that writes to shared files can enforce per‑user rate limits and prevent accidental overwrites by validating tool access before execution. By integrating seamlessly with existing MCP workflows, Shield MCP lets developers focus on business logic while delegating security concerns to a proven middleware layer.