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 Langfuse Observability without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool performs data retrieval operations only. It fetches previously stored exam results without modifying, deleting, or executing any operations. The filtering capabilities allow selective querying but remain read-only. No side effects, no state changes, no destructive or financial operations are possible.
From the tool's definition Tool description explicitly states 'Retrieve the full, untruncated proctor exam result' and 'provides on-demand access to the complete result data'. Uses filtering parameters 'section' and 'mirror_id' to drill into specific parts without side effects.
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
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 Langfuse Observability MCP Server, which means it retrieves data without modifying state.
Register the Langfuse Observability 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 Langfuse Observability. 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 Langfuse Observability MCP server (langfuse-observability-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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