$ loading_
Enforce policy checks on AI tool calls without exposing system credentials.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-governance-proxy" yet — see the docs or source repo.
Design a setup for my AI assistant to access Slack and GitHub through mcp-governance-proxy. Every tool call must be policy-checked first, allowed actions must be restricted, and no API credentials should be exposed to the agent. Provide policy design points, permission boundaries, and the integration flow.
A secure integration plan covering policy validation, permission control, and credential isolation.
I want an AI agent to use AWS tools through mcp-governance-proxy, but it must not delete resources, change production environments, or access sensitive accounts. Help me define example governance policies and explain which calls should be allowed, denied, or require human approval.
A set of cloud governance policy examples with allow, deny, and approval rules.
Explain how to use mcp-governance-proxy to build an auditable tool-call workflow for an AI agent, including request logging, policy decision results, execution logs, and exception alerts, suitable for internal enterprise compliance reviews.
An auditable governance workflow design covering logging, review traceability, and alerting.
Enforce permissions, approvals, sanitization, and audit for AI agent MCP calls.
Govern agent decisions with auditable evidence, confidence calibration, and policy-based handoffs.
Proxy multiple MCP servers while reducing token usage with on-demand tool loading.
Securely lets AI agents access authenticated services without exposing secrets.
Securely proxy OAuth for MCP servers to connect with Claude and ChatGPT.
Provides compliance guardrails for AI agents to operate safely within regulations.