Retrieve the full, untruncated proctor exam result stored by a prior run_exam_for_mirror call. run_exam_for_mirror returns a truncated summary to keep the response within MCP size limits. This tool provides on-demand access to the complete result data, including full tool input schemas and detail...
AI agents call get_exam_result to retrieve information from Playwright Stealth without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and returns previously stored exam results. It has no side effects, does not execute code or commands, does not create or modify data, and does not delete anything. It is purely a data retrieval mechanism with optional filtering parameters. This fits the Read category definition: 'retrieves or queries data; no side effects.'
From the tool's definition Tool description explicitly states it "Retrieve[s] the full, untruncated proctor exam result" and "provides on-demand access to the complete result data." It supports "filtering by section" and "mirror_id" to query existing stored results without modification.
Risk signalsBulk/mass operation — affects multiple targets
Documented attack patterns abuse exactly the kind of access get_exam_result gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Playwright Stealth, and nothing reaches the server without passing your rules. This is the rule we recommend for get_exam_result:
{
"version": "1",
"default": "deny",
"tools": {
"get_exam_result": {}
}
}get_exam_result is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
Free to start. No card required.
Retrieve the full, untruncated proctor exam result stored by a prior run_exam_for_mirror call. run_exam_for_mirror returns a truncated summary to keep the response within MCP size limits. This tool provides on-demand access to the complete result data, including full tool input schemas and detailed exam output. Supports filtering by section (exam_results, logs, summary, errors) and by mirror_id to drill into specific parts of the result without loading the entire payload. Tip: For servers with many tools, the full result can be very large. Use the section and/or mirror_id filters to retrieve only the data you need. Typical usage: 1. Call run_exam_for_mirror — note the returned result_id 2. Call get_exam_result with that result_id and a section filter (e.g., section=. It is categorised as a Read tool in the Playwright Stealth MCP Server, which means it retrieves data without modifying state.
Register the Playwright Stealth MCP server in PolicyLayer and add a rule for get_exam_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 Playwright Stealth. Nothing to install.
get_exam_result is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the get_exam_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 get_exam_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.
get_exam_result is provided by the Playwright Stealth MCP server (pulsemcp/mcp-servers). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Playwright Stealth, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
68 Playwright Stealth tools catalogued and risk-classified — across an index of 43,000+ MCP servers.