embeddings_rabitq_search
Search via RaBitQ quantized index (fast Hamming scan). Returns candidate IDs for reranking. Use when text similarity matters beyond keyword match — native Grep finds exact strings, embeddings find meaning. Pair with memory_store / agentdb_pattern-search to land the vector against your knowledge b...
This record as markdown: /tools/io-github-ruvnet-claude-flow/embeddings-rabitq-search.md
What embeddings_rabitq_search does on Claude Flow
AI agents call embeddings_rabitq_search to retrieve information from Claude Flow without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why embeddings_rabitq_search is rated Low
The tool is a vector similarity search function that retrieves matching IDs from an index without side effects. No data is created, modified, deleted, executed, or committed. The low severity reflects minimal blast radius: misuse would retrieve irrelevant results but cannot corrupt state, trigger external actions, or cause harm.
From the tool's definition Tool performs 'Search via RaBitQ quantized index' and 'Returns candidate IDs for reranking' — a retrieval operation with no modification, creation, or deletion of data.
Attacks that exploit this kind of access
The rule that runs embeddings_rabitq_search 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 embeddings_rabitq_search, this is the rule to start with:
embeddings_rabitq_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 Claude Flow, apply this rule, and every embeddings_rabitq_search call is checked against it from then on.
Questions about embeddings_rabitq_search
Search via RaBitQ quantized index (fast Hamming scan). Returns candidate IDs for reranking. Use when text similarity matters beyond keyword match — native Grep finds exact strings, embeddings find meaning. Pair with memory_store / agentdb_pattern-search to land the vector against your knowledge base. For literal symbol search, native Grep is faster. It is categorised as a Read tool in the Claude Flow MCP Server, which means it retrieves data without modifying state.
Register the Claude Flow MCP server in PolicyLayer and add a rule for embeddings_rabitq_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 Claude Flow. Nothing to install.
embeddings_rabitq_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 embeddings_rabitq_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 embeddings_rabitq_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.
embeddings_rabitq_search 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.
More on Claude Flow, and thousands of servers like it.
This server
Across the catalogue