embeddings_hyperbolic
Hyperbolic embedding operations (Poincaré ball) 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 ...
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What embeddings_hyperbolic does on Claude Flow
AI agents call embeddings_hyperbolic 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_hyperbolic is rated Low
embeddings_hyperbolic performs mathematical transformations on text to generate semantic embeddings and match them against stored vectors. This is a read-only retrieval operation with no side effects on data state, external systems, or financial obligations. The hyperbolic geometry method (Poincaré ball) is an implementation detail that does not change the fundamental read-only nature of the operation.
From the tool's definition Tool description emphasizes "embedding operations" and "similarity" matching — it retrieves or computes vector representations of text for comparison against a knowledge base. No data modification, deletion, code execution, or financial impact.
Attacks that exploit this kind of access
The rule that runs embeddings_hyperbolic 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_hyperbolic, this is the rule to start with:
embeddings_hyperbolic 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_hyperbolic call is checked against it from then on.
Questions about embeddings_hyperbolic
Hyperbolic embedding operations (Poincaré ball) 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_hyperbolic: 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_hyperbolic 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_hyperbolic 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_hyperbolic. 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_hyperbolic 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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