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Enforce permissions, approvals, sanitization, and audit for AI agent MCP calls.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-guardian" yet — see the docs or source repo.
Design a guard layer for my MCP tool calls: require human approval for delete, write, or external data transfer actions, allow read-only actions to pass directly, and keep an audit trail.
A risk-based control plan for tool calls, including approval rules and audit requirements.
I want to proxy an AI agent's MCP requests locally and sanitize parameters before forwarding, removing sensitive information or dangerous arguments. Outline a policy approach.
A local proxy sanitization policy describing which parameters should be filtered, redacted, or blocked.
Help me plan an audit mechanism for MCP tool calls that records who initiated the call, which tool was used, whether it was approved, and the final outcome.
A checklist of audit fields and logging flow for traceability and compliance review.
Developers or DevOps teams can place a gateway between AI agents and MCP tools to apply permission, sanitization, and approval rules. This reduces the risk of unsafe or unauthorized tool usage.
When an agent may perform deletes, writes, or other high-risk actions, teams can require human approval before execution. This is useful for adding protection around production systems or critical data.
If a team needs to track which MCP tools an AI agent called and whether calls were blocked or approved, this gateway can provide an auditable path. That helps with troubleshooting and governance.
It is a gateway for AI agent MCP tool calls that enforces permissions, sanitization, human approval, and audit. The description also says it includes a policy engine and a local proxy CLI.
The provided information only indicates that it includes a policy engine and a local proxy CLI. Specific installation steps, runtime requirements, or API key needs are not stated; see the source repository.
Based on the description, it is not a single business tool but a control gateway sitting between AI agents and MCP tool calls. Its focus is governance and security, including permissions, sanitization, approval, and audit.
Enforce policy checks on AI tool calls without exposing system credentials.
Securely expose OpenAPI endpoints as MCP tools with auth, limits, and auditing.
Gate MCP agent actions through compliance policies before execution.
Intercept and block MCP tool calls with YAML policies for safer AI agents.
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.