Gatekeeper MCP server

Runestone Agent Gatekeeper

A policy-based gatekeeper service that sits between AI agents and real-world tools (shell, HTTP, filesystem), enforcing approvals, denials, USD budgets, and audit logging.

Who Is This For

If you're running AI agents (Claude, GPT, local LLMs, MCP servers) that execute tool calls against real systems — and you're responsible for what happens when those calls go wrong — Gatekeeper is for you. Specifically:

• You build agentic apps and your agent can run shell commands, write files, or hit external APIs.
• You've already had one "why did it do that?" moment — or you're staring down the possibility.
• You want a self-hostable, auditable, single-binary boundary between your agent and the world, without adopting an enterprise SSO stack or wiring everything through a cloud proxy.
• You need USD budgets and signed approvals as first-class primitives, not features bolted onto an observability tool.

You were probably using: nothing (and trusting the prompt), ad-hoc Python wrappers around subprocess, a cloud LLM gateway, or shell-level sudoers rules that don't understand tool semantics.

What Problem This Solves

AI agents need to execute actions in the real world: running shell commands, writing files, making HTTP requests. Without guardrails, an agent can accidentally (or adversarially) execute dangerous operations.

The Gatekeeper intercepts all tool requests and:

• Allows low-risk operations immediately
• Denies operations that match dangerous patterns
• Requires human approval for sensitive operations
• Catches sensitive-boundary crossings — Keychain, SSH keys, cloud credentials, browser profiles, package-registry tokens, env files (see Sensitive Boundary Protection)
• Rejects when a USD / token / call budget is exceeded (optional) — per actor (a rolling guardrail) or per run (scope: run, keyed on runId: caps a single agentic run at the action boundary, where recursive burn compounds)
• Optionally proxies Anthropic model calls — route inference through the gatekeeper so every /v1/messages call is audited, the API key stays centralized, and real per-token cost is metered onto the audit row and into budgets (off by default; see docs/API.md)

All decisions are logged to an append-only audit trail (JSONL or Postgres). JSONL receipts include a sequence, previous-entry hash, and entry hash for independent tamper/gap verification. An aggregation endpoint (/usage) exposes call counts — and real cost/token sums — by actor × tool × day. A budget endpoint (/budget) surfaces current spend vs cap per configured rule.

Gatekeeper only controls actions that are actually routed through it. It does not infer coverage from installation. Verify the client's actual routing configuration before describing a runtime as protected.

Setup CLI

The gatekeeper CLI previews every client configuration change, creates timestamped backups before applying one, and can restore the last patch:

npm install --global @runestone-labs/gatekeeper
gatekeeper init --client claude-code
gatekeeper doctor
gatekeeper verify ./data/audit

Supported setup targets are claude-code, openclaw, and mcp. Fresh Claude Code patches set GATEKEEPERFAILCLOSED=1. Existing fail-open hooks remain usable, but doctor reports them as a protection failure with the exact migration command.

Shortened. The full README is on GitHub.