embeddings_neural
Neural substrate operations (RuVector integration) 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 Gr...
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What embeddings_neural does on Claude Flow
AI agents call embeddings_neural 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_neural is rated Low
This tool retrieves and queries vector embeddings to find semantically similar content. It searches a knowledge base rather than creating, modifying, executing code, deleting data, or moving money. The mechanism (neural embeddings via RuVector) does not change the fundamental Read category—it is still a retrieval operation with no side effects on data or systems.
From the tool's definition The tool performs "neural substrate operations" and "text similarity" analysis paired with "pattern-search" and "knowledge base" queries.
Attacks that exploit this kind of access
The rule that runs embeddings_neural 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_neural, this is the rule to start with:
embeddings_neural 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_neural call is checked against it from then on.
Questions about embeddings_neural
Neural substrate operations (RuVector integration) 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_neural: 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_neural 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_neural 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_neural. 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_neural 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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