tool-creator

To create secure MCP tools, use Pydantic input models for strict validation, apply SQL and path sanitization routines, and implement secrets-safe handling to prevent injection attacks and data leaks. This ensures only sanitized, validated inputs reach your database, cloud, or file system integrations.

The best way to add rate limiting and audit logging to MCP tools is to apply configurable rate limits and built-in audit logging during tool creation. This enforces production stability by throttling access and recording invocations for observability and compliance.

Yes, Pydantic validation protects MCP tools executing PostgreSQL queries by enforcing strict input models. You can scaffold a read-only query tool that restricts SELECT-only queries, sanitizes SQL inputs, rate limits access, and masks sensitive values in output results.

Yes, this security-first approach works for MCP tools interfacing with cloud services, communication APIs, file systems, and databases. It provides boilerplates and examples for webhook handlers, deploy managers, and messaging integrations requiring safety and observability.

Prevent sensitive data leaks in MCP tools by implementing secrets-safe handling, masking sensitive values in outputs, and applying graceful error handling. These operational safeguards ensure production stability and prevent accidental exposure of secrets during tool invocation.