search_learnings
[DEPRECATED: Use search_all_knowledge instead] Search past learnings. PREFER search_all_knowledge which searches learnings + recon findings + gaps in a unified query.
This record as markdown: /tools/io-github-homenshum-nodebench/search-learnings.md
What search_learnings does on Nodebench
AI agents call search_learnings to retrieve information from Nodebench without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why search_learnings is rated Low
This tool retrieves or queries historical learning data without modifying, deleting, or executing external operations. It is a straightforward search/read operation. Even though deprecated, its function remains a simple data retrieval task with minimal blast radius if misused by an AI agent.
From the tool's definition Tool name is 'search_learnings' and description indicates it 'Search[es] past learnings' — a query/retrieval operation with no side effects. The deprecated status and recommendation to use an alternative function do not change the core operation.
Attacks that exploit this kind of access
The rule that runs search_learnings 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 search_learnings, this is the rule to start with:
search_learnings is read-only, so it stays allowed. 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 search_learnings call is checked against it from then on.
Questions about search_learnings
[DEPRECATED: Use search_all_knowledge instead] Search past learnings. PREFER search_all_knowledge which searches learnings + recon findings + gaps in a unified query. It is categorised as a Read tool in the Nodebench MCP Server, which means it retrieves data without modifying state.
Register the Nodebench MCP server in PolicyLayer and add a rule for search_learnings: 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.
search_learnings 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 search_learnings 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 search_learnings. 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.
search_learnings 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.
More on Nodebench, and thousands of servers like it.
This server
Across the catalogue