Perform a KNN vector similarity search using Redis 8 or later version on vectors stored in hash data structures. Args: query_vector: List of floats to use as the query vector. index_name: Name of the Redis index. Unless specifically specified, use the default index name. vector_field: Name of the...
AI agents call vector_search_hash to retrieve information from Redis MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
vector_search_hash is a search/query operation that retrieves matching vectors and documents based on similarity metrics. It takes a query vector and returns results without any side effects. No data is created, modified, or deleted. This clearly falls under the Read category with low severity, as it only retrieves information from Redis.
From the tool's definition Tool performs 'KNN vector similarity search' and 'Returns a list of matched documents' - operations that retrieve and query data without modification, creation, or deletion.
Documented attack patterns abuse exactly the kind of access vector_search_hash gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Redis MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for vector_search_hash:
{
"version": "1",
"default": "deny",
"tools": {
"vector_search_hash": {}
}
}vector_search_hash is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Perform a KNN vector similarity search using Redis 8 or later version on vectors stored in hash data structures. Args: query_vector: List of floats to use as the query vector. index_name: Name of the Redis index. Unless specifically specified, use the default index name. vector_field: Name of the indexed vector field. Unless specifically required, use the default field name k: Number of nearest neighbors to return. return_fields: List of fields to return (optional). Returns: A list of matched documents or an error message. It is categorised as a Read tool in the Redis MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Redis MCP Server MCP server in PolicyLayer and add a rule for vector_search_hash: 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 Redis MCP Server. Nothing to install.
vector_search_hash 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 vector_search_hash 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 vector_search_hash. 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.
vector_search_hash is provided by the Redis MCP Server MCP server (redis/mcp-redis). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 53 Redis MCP Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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53 Redis MCP Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.