ruvector-optimize
Self-learning query optimization and index tuning
This record as markdown: /tools/io-github-ruvnet-claude-flow/ruvector-optimize.md
What ruvector-optimize does on Claude Flow
AI agents invoke ruvector-optimize to trigger actions in Claude Flow. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why ruvector-optimize is rated High
Index tuning implies structural changes to database indexes (creating, dropping, or rebuilding indexes) and altering query plans. This goes beyond read-only analysis and involves executing optimization operations that modify database internals. In an enterprise AI orchestration context, an autonomous 'self-learning' optimizer could make broad, difficult-to-reverse schema-level changes, raising the severity to high.
From the tool's definition 'Self-learning query optimization and index tuning' — actively modifies index structures and query execution plans
Attacks that exploit this kind of access
The rule that runs ruvector-optimize safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Claude Flow, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For ruvector-optimize, this is the rule to start with:
ruvector-optimize stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Claude Flow, apply this rule, and every ruvector-optimize call is checked against it from then on.
Questions about ruvector-optimize
Self-learning query optimization and index tuning. It is categorised as a Execute tool in the Claude Flow MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Claude Flow MCP server in PolicyLayer and add a rule for ruvector-optimize: 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 Claude Flow. Nothing to install.
ruvector-optimize is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the ruvector-optimize 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 ruvector-optimize. 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.
ruvector-optimize is provided by the Claude Flow MCP server (claude-flow). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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