dry_run

Show what would be pruned without actually pruning (optional, default: false) (boolean, optional)

SERVERGitHub SOURCEoci:ghcr.io/aifity/omnigit-mcp:0.5.0

Low RISK CLASS

Category Read

Parameters 00 required

Recommended Allowedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-aifity-omnigit-mcp/dry-run.md

WHAT IT DOES

What dry_run does on GitHub

AI agents call dry_run to retrieve information from GitHub without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

RISK

Why dry_run is rated Low

The tool is a dry-run flag/option that simulates a pruning operation without executing it, meaning it only shows what would happen rather than making any changes. This is a read/preview operation with no side effects. Confidence is moderate because the description appears to describe a parameter rather than a standalone tool, making the full context unclear.

From the tool's definition Show what would be pruned without actually pruning

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs dry_run safely

PolicyLayer is an MCP gateway: it sits between your AI agents and GitHub, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For dry_run, this is the rule to start with:

dry_run Allowed

dry_run is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

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

ALLOW ONLY THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect GitHub, apply this rule, and every dry_run call is checked against it from then on.

FAQ

Questions about dry_run

What does the dry_run tool do? +

Show what would be pruned without actually pruning (optional, default: false) (boolean, optional). It is categorised as a Read tool in the GitHub MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on dry_run? +

Register the GitHub MCP server in PolicyLayer and add a rule for dry_run: 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 GitHub. Nothing to install.

What risk level is dry_run? +

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

Can I rate-limit dry_run? +

Yes. Add a rate_limit block to the dry_run 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 dry_run completely? +

Set action: deny in the PolicyLayer policy for dry_run. 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 dry_run? +

dry_run is provided by the GitHub MCP server (oci:ghcr.io/aifity/omnigit-mcp:0.5.0). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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