AI agents use add_result 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 'add_' prefix indicates a create/insert operation that modifies TestRail data by adding a new result record. This is reversible (results can be deleted via sibling 'delete_' tools), making it Write rather than Destructive. The blast radius is medium—incorrect test results could mislead testing decisions and quality metrics, but the operation itself is not inherently dangerous or irreversible.
From the tool's definition Tool name is 'add_result' which performs a create/add operation. Sibling tools include 'add_case', 'add_dataset', 'add_project', 'add_run', 'add_section' (all Write operations) and destructive counterparts like 'delete_case', 'delete_dataset', etc.
Documented attack patterns abuse exactly the kind of access add_result 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 add_result:
{
"version": "1",
"default": "deny",
"tools": {
"add_result": {
"limits": [
{
"counter": "add_result_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}add_result 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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add_result. 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 add_result: 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.
add_result 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_result 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_result. 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_result 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.