Agent Guardrail MCP Server
Enforce what your autonomous agent is allowed to execute—tool calls, writes, and side effects—not just how it phrases replies.
Overview
Agent Guardrail is a Ship-phase MCP server that enforces action-level governance so AI agents cannot perform disallowed tool executions.
What is this MCP server?
- Action-level governance: policies on what agents DO, not what they SAY
- Stdio MCP server installable from PyPI package agent-guardrail 0.1.2
- Fits agent workflows in Claude Code, Cursor, and other MCP clients
- GitHub source at eren-solutions/agent-guardrail
- Early-stage 0.1.x release aimed at execution control
- Published version 0.1.2 on PyPI
- Transport: stdio MCP
- Registry identifier: agent-guardrail
What problem does it solve?
Autonomous agents can sound careful in chat yet still run destructive or out-of-scope tool actions without execution-time policy.
Who is it for?
Solo builders shipping agent-heavy workflows who need execution policy separate from prompt instructions.
Skip if: Teams that only need content moderation, static secret scanning, or governance with no MCP-based agents.
What do I get? / Deliverables
After installing the stdio MCP server, agent tool calls can be gated by guardrails so only permitted actions run in your dev environment.
- Stdio MCP guardrail server wired into the agent toolchain
- Execution-time constraints on agent actions
- Clearer separation between conversational tone and permitted operations
Recommended MCP Servers
Journey fit
How it compares
Execution-policy MCP server, not a prompt template pack or traditional WAF.
Common Questions / FAQ
Who is Agent Guardrail for?
Indie developers and small teams running MCP-enabled coding agents that need hard limits on tool actions before production exposure.
When should I use Agent Guardrail?
Use it during Ship security hardening and ongoing Operate when agents can call tools that modify infra, data, or repos.
How do I add Agent Guardrail to my agent?
Install the PyPI package agent-guardrail (0.1.2), configure stdio MCP in your client per the GitHub README, and restart the agent session.