review_auto

THE RISK

Low Risk

Why review_auto needs a policy

Code review is fundamentally a read-only analytical operation. The tool examines diffs (existing code changes or git workspace state) to provide feedback, but does not execute code, modify files, delete data, or trigger external side effects.

From the tool's definition Tool performs automated code review via 'review_diff' or 'review_git_diff'. It reads and analyzes code diffs without modifying code, git history, or data. 'Review' and 'analysis' operations retrieve information about code quality/issues without side effects.

Documented attack patterns abuse exactly the kind of access review_auto gives an agent:

POLICY

How to control review_auto

PolicyLayer is an MCP gateway — it sits between your AI agents and Context Engine MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for review_auto:

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "review_auto": {}
  }
}

review_auto is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.

Create a free account and register Context Engine MCP Server — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

CAP THIS TOOL →

Free to start. No card required.

EXPLORE

Related tools and policies

More Context Engine MCP Server tools

Destructive clear_index Remove saved index state and clear caches without rebuilding. Destructive delete_plan Delete a saved plan from storage. Destructive reindex_workspace Clear current index state and rebuild it from scratch. Execute execute_plan Execute steps from an implementation plan, generating code changes. This tool orchestrate Execute index_workspace Index the current workspace for semantic search. This tool scans all source files in the Execute reactive_review_pr Start a reactive PR code review session. This tool initiates an AI-powered code review wi Execute run_static_analysis Run local static analyzers (tsc and optional semgrep) and return structured findings. Execute check_invariants Run YAML invariants deterministically against a unified diff (no LLM).

All 50 Context Engine MCP Server tools →

Read tools on other servers

M-Team MCP Server get_torrent_detail YouTube MCP Server search_channel Android Forensics ADB MCP Server parse_browser_history 0xarchive web3_challenge

Go deeper

The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Data exfiltration → How read access becomes an exfiltration channel when chained with untrusted content.
MCP token cost → What connecting this server costs in context-window tokens on every request.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about review_auto

What does the review_auto tool do? +

Smart wrapper that chooses review_diff when a diff is provided; otherwise chooses review_git_diff for the current git workspace. It is categorised as a Read tool in the Context Engine MCP Server MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on review_auto? +

Register the Context Engine MCP Server MCP server in PolicyLayer and add a rule for review_auto: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Context Engine MCP Server. Nothing to install.

What risk level is review_auto? +

review_auto is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit review_auto? +

Yes. Add a rate_limit block to the review_auto rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block review_auto completely? +

Set action: deny in the PolicyLayer policy for review_auto. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides review_auto? +

review_auto is provided by the Context Engine MCP Server MCP server (kirachon/context-engine). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Context Engine MCP Server tool call.

Start from Context Engine MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

CHECK YOUR STACK →