assignees

Usernames to assign to this issue (string[], optional)

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

Medium RISK CLASS

Category Write

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

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

WHAT IT DOES

What assignees does on GitHub

AI agents use assignees to create or update resources in GitHub, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your GitHub environment.

RISK

Why assignees is rated Medium

This tool creates or modifies issue data (assigning users) in a reversible manner. It is a Write operation rather than Read (it changes state), not Execute (it does not run code or arbitrary operations), not Destructive (assignees can be removed), and not Financial.

From the tool's definition The tool assigns users to an issue, which modifies issue state by adding assignees. The description 'Usernames to assign to this issue' indicates a write operation that changes issue metadata.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs assignees 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 assignees, this is the rule to start with:

assignees Rate-limited

assignees stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "assignees": {
      "limits": [
        {
          "counter": "assignees_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

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

FAQ

Questions about assignees

What does the assignees tool do? +

Usernames to assign to this issue (string[], optional). It is categorised as a Write tool in the GitHub MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on assignees? +

Register the GitHub MCP server in PolicyLayer and add a rule for assignees: 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 assignees? +

assignees is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit assignees? +

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

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

assignees 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.

KEEP EXPLORING

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