AI agents use close_run to create or update resources in TestRail MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your TestRail MCP Server environment.
The tool performs a reversible state modification (closing/completing a test run) rather than deletion or data retrieval. This aligns with the Write category. Severity is medium because closing a run in a testing system could disrupt workflows or prevent further test submissions, but the action remains reversible (runs can typically be reopened).
From the tool's definition Tool name 'close_run' indicates a state-change operation on a test run in TestRail. Related sibling tools include 'add_run' (Write) and 'delete_run' (Destructive), suggesting this server manages test execution lifecycle.
Documented attack patterns abuse exactly the kind of access close_run gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and TestRail MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for close_run:
{
"version": "1",
"default": "deny",
"tools": {
"close_run": {
"limits": [
{
"counter": "close_run_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}close_run 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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close_run. It is categorised as a Write tool in the TestRail MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the TestRail MCP Server MCP server in PolicyLayer and add a rule for close_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 TestRail MCP Server. Nothing to install.
close_run 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 close_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.
Set action: deny in the PolicyLayer policy for close_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.
close_run is provided by the TestRail MCP Server MCP server (sker65/testrail-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from TestRail 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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29 TestRail MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.