agentdb_hierarchical-recall
Recall from hierarchical memory with optional tier filter Use when generic memory_* tools are wrong because you need AgentDB-specific controllers (HNSW vector search, hierarchical tiers, causal-graph links, pattern store/recall, RaBitQ quantization). For simple key-value persistence, memory_store...
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What agentdb_hierarchical-recall does on Ruflo
AI agents call agentdb_hierarchical-recall 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 agentdb_hierarchical-recall is rated Low
The tool performs a read/query operation against a hierarchical memory system (vector search, tiered recall). It retrieves stored information without modifying or deleting it. Severity is medium because it accesses potentially sensitive agent memory across hierarchical tiers and a swarm intelligence system, meaning misuse could expose internal agent state, reasoning chains, or sensitive context stored in memory.
From the tool's definition 'Recall from hierarchical memory with optional tier filter' — this is a retrieval operation using HNSW vector search across hierarchical memory tiers
Attacks that exploit this kind of access
The rule that runs agentdb_hierarchical-recall 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 agentdb_hierarchical-recall, this is the rule to start with:
agentdb_hierarchical-recall 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 agentdb_hierarchical-recall call is checked against it from then on.
Questions about agentdb_hierarchical-recall
Recall from hierarchical memory with optional tier filter Use when generic memory_* tools are wrong because you need AgentDB-specific controllers (HNSW vector search, hierarchical tiers, causal-graph links, pattern store/recall, RaBitQ quantization). For simple key-value persistence, memory_store/memory_retrieve are simpler. For unrelated file work, native Read/Write are fine. 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 agentdb_hierarchical-recall: 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.
agentdb_hierarchical-recall 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 agentdb_hierarchical-recall 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 agentdb_hierarchical-recall. 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.
agentdb_hierarchical-recall 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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