GCP MCP Server
Model Context Protocol server for Google Cloud Platform operations, enabling AI applications to manage cloud infrastructure.
Problem Statement
AI applications needed a way to interact with Google Cloud Platform services through a standardized protocol. This would enable AI assistants to help users manage cloud resources, deploy applications, and monitor infrastructure.
Solution Approach
We created an MCP server that provides comprehensive GCP functionality:
- Compute Engine instance management
- Cloud Storage operations
- Cloud SQL database management
- IAM and security operations
- Monitoring and logging access
- Resource provisioning via Terraform
Technologies Used
- Python 3.11+ - Core language
- FastAPI - MCP server framework
- Google Cloud SDK - Official GCP client libraries
- Terraform - Infrastructure as Code
- MCP Protocol - Model Context Protocol
Key Achievements
- ✅ Multi-service GCP integration
- ✅ Infrastructure automation support
- ✅ Secure credential management
- ✅ Comprehensive error handling
- ✅ Resource lifecycle management
Impact
Enables AI applications to:
- Provision and manage cloud infrastructure
- Deploy applications to GCP
- Monitor cloud resources
- Automate DevOps workflows
Lessons Learned
- Cloud API complexity requires careful abstraction
- Security and credential management is critical
- Infrastructure as Code integration adds significant value
- Multi-service APIs benefit from unified interfaces