deeptrace_log_receipt
Log a tamper-evident action receipt. Every agent action should produce a receipt recording what was done, what policy allowed it, and what evidence supports the result. The receipt is content-addressed via SHA-256.
This record as markdown: /tools/io-github-homenshum-nodebench/deeptrace-log-receipt.md
What deeptrace_log_receipt does on Nodebench
AI agents use deeptrace_log_receipt to create or update resources in Nodebench, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Nodebench environment.
Why deeptrace_log_receipt is rated Medium
This tool writes a new receipt/log entry recording agent actions. It is a write operation (creating an immutable audit log record) with no destructive, financial, or execution side-effects. Severity is low because it only appends audit metadata; misuse has minimal blast radius.
From the tool's definition 'Log a tamper-evident action receipt' and 'The receipt is content-addressed via SHA-256' — creates/stores a new audit record
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs deeptrace_log_receipt safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Nodebench, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For deeptrace_log_receipt, this is the rule to start with:
deeptrace_log_receipt 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Nodebench, apply this rule, and every deeptrace_log_receipt call is checked against it from then on.
Questions about deeptrace_log_receipt
Log a tamper-evident action receipt. Every agent action should produce a receipt recording what was done, what policy allowed it, and what evidence supports the result. The receipt is content-addressed via SHA-256. It is categorised as a Write tool in the Nodebench MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Nodebench MCP server in PolicyLayer and add a rule for deeptrace_log_receipt: 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 Nodebench. Nothing to install.
deeptrace_log_receipt 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 deeptrace_log_receipt 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 deeptrace_log_receipt. 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.
deeptrace_log_receipt is provided by the Nodebench MCP server (nodebench-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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