embeddings_search
Semantic search across stored embeddings 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/hamza-agent-hub-252/embeddings-search.md
What embeddings_search does on Hamza Agent Hub 252
AI agents call embeddings_search to retrieve information from Hamza Agent Hub 252 without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why embeddings_search is rated Low
This tool retrieves and queries data (semantic search over embeddings) without any side effects, data modification, or irreversible operations. It is a pure retrieval mechanism analogous to search or filter operations, making it clearly a Read category risk.
From the tool's definition Tool name 'embeddings_search' and description explicitly states it performs 'semantic search across stored embeddings' and 'finds meaning' without modifying or deleting data.
Attacks that exploit this kind of access
The rule that runs embeddings_search safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Hamza Agent Hub 252, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For embeddings_search, this is the rule to start with:
embeddings_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 Hamza Agent Hub 252, apply this rule, and every embeddings_search call is checked against it from then on.
Questions about embeddings_search
Semantic search across stored embeddings 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 Hamza Agent Hub 252 MCP Server, which means it retrieves data without modifying state.
Register the Hamza Agent Hub 252 MCP server in PolicyLayer and add a rule for embeddings_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 Hamza Agent Hub 252. Nothing to install.
embeddings_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_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_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_search is provided by the Hamza Agent Hub 252 MCP server (hamzachallenge3-dev/hamza-agent-hub-252). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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