Remove assignees from an issue
AI agents use remove_assignees_from_issue to create or update resources in GitHub Repos Manager MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your GitHub Repos Manager MCP Server environment.
Removing assignees from an issue is a reversible modification to issue metadata (assignees can be re-added). This is a Write operation. It does not delete data permanently, execute code, or involve finances. Severity is medium as misuse could disrupt issue tracking and assignment workflows.
From the tool's definition 'Remove assignees from an issue' — modifies issue metadata by removing assignees
Documented attack patterns abuse exactly the kind of access remove_assignees_from_issue gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and GitHub Repos Manager MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for remove_assignees_from_issue:
{
"version": "1",
"default": "deny",
"tools": {
"remove_assignees_from_issue": {
"limits": [
{
"counter": "remove_assignees_from_issue_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
} remove_assignees_from_issue 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.
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Remove assignees from an issue. It is categorised as a Write tool in the GitHub Repos Manager MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the GitHub Repos Manager MCP Server MCP server in PolicyLayer and add a rule for remove_assignees_from_issue: 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 Repos Manager MCP Server. Nothing to install.
remove_assignees_from_issue is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the remove_assignees_from_issue 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.
Set action: deny in the PolicyLayer policy for remove_assignees_from_issue. 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.
remove_assignees_from_issue is provided by the GitHub Repos Manager MCP Server MCP server (kurdin/github-repos-manager-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from GitHub Repos Manager MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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84 GitHub Repos Manager MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.