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Manage Railway projects in natural language with approval guardrails for risky actions.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Railway Guardrail MCP" yet — see the docs or source repo.
Connect to my Railway project and list the current status of all services, the latest deployment time, and whether there are any failed deployments.
A list of project services, their runtime status, recent deployment details, and any failed or abnormal items highlighted.
Help me update API_BASE_URL in production to a new endpoint; if this could affect the live service, explain the risk first and wait for my approval before doing anything.
A risk and impact assessment first, with human approval required before any high-risk change is applied.
Investigate why the most recent deployment in this Railway project failed, summarize relevant logs, identify possible root causes, and suggest next steps to fix it.
An analysis of the failure, a summary of key logs, and a practical list of recommended fixes.
Secure MCP servers with policy checks, redaction, access control, and audit logs
Deploy, manage, and monitor OpenWebUI on Railway using natural language commands.
Protect MCP-connected agents with PII redaction, rate limits, and policy enforcement.
Secure LLM and MCP tool interactions with zero-trust controls and policy enforcement.
Intercept and block MCP tool calls with YAML policies for safer AI agents.
Demo MCP tool for banking PII payloads and external guardrail testing.