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 Ruflo
AI agents call embeddings_neural to retrieve information from Ruflo 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 or queries embedding vectors and performs similarity matching operations. It searches a knowledge base using neural embeddings but does not create, modify, delete, execute arbitrary code, or commit financial operations. The explicit mention of pairing with pattern-search and knowledge base queries confirms it is a read-only retrieval mechanism. No side effects or data mutations are described.
From the tool's definition Tool performs 'neural substrate operations' and 'text similarity' analysis, explicitly contrasted with 'native Grep finds exact strings' suggesting lookup/search functionality.
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 Ruflo, 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 Ruflo, 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 Ruflo MCP Server, which means it retrieves data without modifying state.
Register the Ruflo 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 Ruflo. 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 Ruflo MCP server (ruvnet/ruflo). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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