convex_audit_vector_search
Audit Convex vector search implementation: validates vectorIndex dimensions against known embedding models, checks for missing filterFields, v.array(v.float64()) usage, dimension mismatches between schema and code, and undefined index references.
This record as markdown: /tools/io-github-homenshum-nodebench/convex-audit-vector-search.md
What convex_audit_vector_search does on Nodebench
AI agents call convex_audit_vector_search 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 convex_audit_vector_search is rated Low
This tool audits and validates Convex vector search configurations by inspecting schema and code for configuration errors. It reads and analyzes existing configuration but does not modify data, execute arbitrary code, delete resources, or perform financial operations.
From the tool's definition Tool performs validation and auditing tasks: 'validates vectorIndex dimensions', 'checks for missing filterFields', and 'checks... dimension mismatches' and 'undefined index references'.
Attacks that exploit this kind of access
The rule that runs convex_audit_vector_search 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 convex_audit_vector_search, this is the rule to start with:
convex_audit_vector_search 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 convex_audit_vector_search call is checked against it from then on.
Questions about convex_audit_vector_search
Audit Convex vector search implementation: validates vectorIndex dimensions against known embedding models, checks for missing filterFields, v.array(v.float64()) usage, dimension mismatches between schema and code, and undefined index references. 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 convex_audit_vector_search: 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.
convex_audit_vector_search 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 convex_audit_vector_search 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 convex_audit_vector_search. 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.
convex_audit_vector_search 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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