AI agents use add_comment to create or update resources in GitHub Kanban MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your GitHub Kanban MCP Server environment.
Adding comments to GitHub issues creates new, reversible data. It modifies issue state by appending comments but does not delete, execute arbitrary code, or perform financial operations. This is a Write operation with medium severity—misuse could spam issues or post misleading information, but comments can be deleted/edited, limiting blast radius.
From the tool's definition Tool name 'add_comment' and description indicating it adds comments to tasks. In a GitHub context, this creates new comment data on issues.
Documented attack patterns abuse exactly the kind of access add_comment gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and GitHub Kanban MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for add_comment:
{
"version": "1",
"default": "deny",
"tools": {
"add_comment": {
"limits": [
{
"counter": "add_comment_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}add_comment 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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タスクにコメントを追加. It is categorised as a Write tool in the GitHub Kanban MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the GitHub Kanban MCP Server MCP server in PolicyLayer and add a rule for add_comment: 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 Kanban MCP Server. Nothing to install.
add_comment 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 add_comment 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 add_comment. 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.
add_comment is provided by the GitHub Kanban MCP Server MCP server (sunwood-ai-labs/github-kanban-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from GitHub Kanban 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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4 GitHub Kanban MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.