Retrieve detailed execution trace with all observations and metrics.
AI agents call get_trace to retrieve information from Langfuse Mcp Python without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The verb 'Retrieve' and the query-like nature of fetching trace data (observations and metrics) without modification places this squarely in the Read category. There is no data creation, modification, deletion, or external code execution.
From the tool's definition Tool name 'get_trace' and description 'Retrieve detailed execution trace with all observations and metrics' indicate a retrieval operation with no side effects.
Attacks that exploit this kind of access
Retrieve detailed execution trace with all observations and metrics. It is categorised as a Read tool in the Langfuse Mcp Python MCP Server, which means it retrieves data without modifying state.
Register the Langfuse Mcp Python MCP server in PolicyLayer and add a rule for get_trace: 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 Mcp Python. Nothing to install.
get_trace 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_trace 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_trace. 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_trace is provided by the Langfuse Mcp Python MCP server (log-logn/langfuse-mcp-python). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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