record_learning
Store an edge case, gotcha, pattern, or regression discovered during verification. Learnings are searchable via full-text search and prevent repeating the same mistakes. Always record learnings at the end of a verification cycle.
This record as markdown: /tools/io-github-homenshum-nodebench/record-learning.md
What record_learning does on Nodebench
AI agents use record_learning 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 record_learning is rated Medium
The tool writes/creates a new learning record in a persistent store. It is reversible in principle (records can be deleted), has no destructive or financial implications, and the blast radius of misuse is low — at worst, incorrect or noisy entries are stored in a knowledge base.
From the tool's definition 'Store an edge case, gotcha, pattern, or regression discovered during verification. Learnings are searchable via full-text search and prevent repeating the same mistakes.'
Attacks that exploit this kind of access
The rule that runs record_learning 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 record_learning, this is the rule to start with:
record_learning 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 record_learning call is checked against it from then on.
Questions about record_learning
Store an edge case, gotcha, pattern, or regression discovered during verification. Learnings are searchable via full-text search and prevent repeating the same mistakes. Always record learnings at the end of a verification cycle. 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 record_learning: 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.
record_learning 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 record_learning 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 record_learning. 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.
record_learning 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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