AWS Bedrock Guardrails
This MCP server provides AI assistants with complete AWS Bedrock Guardrails management capabilities, built using Python with boto3 and FastMCP for programmatic control over all guardrail policy types including content filtering, topic restrictions, word blocking, sensitive information detection, and contextual grounding policies. The implementation offers full CRUD operations for guardrails with support for all AWS policy configurations, plus a unique Terraform export feature that generates CloudFormation-compatible .tf files for infrastructure-as-code workflows, enabling seamless integration between conversational AI management and DevOps deployment pipelines. Built with secure environment-based AWS credential handling and comprehensive error management, it serves enterprise teams needing natural language access to Bedrock safety controls, DevOps engineers requiring AI-driven guardrail configuration with IaC export capabilities, and compliance teams where conversational policy management enhances LLM safety governance without direct AWS console knowledge.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- cloud-infra
- Source
- PulseMCP
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve AWS Bedrock Guardrails from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.