embeddings_compare
Compare similarity between two texts 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.
This record as markdown: /tools/io-github-ruvnet-claude-flow/embeddings-compare.md
What embeddings_compare does on Claude Flow
AI agents call embeddings_compare 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_compare is rated Low
embeddings_compare retrieves or computes a similarity metric between two provided texts without side effects. It does not execute code, modify data, delete anything, or commit financial obligations. It is a pure query/analysis tool in the Read category with low severity since misuse would only produce incorrect similarity scores without affecting system state or data integrity.
From the tool's definition Tool performs comparison and similarity analysis on text inputs with no modification, creation, or deletion of data.
Attacks that exploit this kind of access
The rule that runs embeddings_compare 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_compare, this is the rule to start with:
embeddings_compare 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_compare call is checked against it from then on.
Questions about embeddings_compare
Compare similarity between two texts 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_compare: 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_compare 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_compare 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_compare. 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_compare 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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