agentdb_pattern-store
Store a pattern directly via ReasoningBank controller 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/mem...
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What agentdb_pattern-store does on Ruflo
AI agents use agentdb_pattern-store to create or update resources in Ruflo, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Ruflo environment.
Why agentdb_pattern-store is rated Medium
This tool creates or modifies data in a pattern storage system (AgentDB with HNSW vector search, hierarchical tiers, causal-graph links). The action is reversible via recall/retrieval or update operations, not destructive. While it affects agent swarm memory and reasoning, it does not execute code, delete data irreversibly, or move money.
From the tool's definition Tool name contains 'store' and description explicitly states 'Store a pattern directly' via ReasoningBank controller, indicating data modification/creation in AgentDB-specific storage systems.
Attacks that exploit this kind of access
The rule that runs agentdb_pattern-store 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_pattern-store, this is the rule to start with:
agentdb_pattern-store stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. 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_pattern-store call is checked against it from then on.
Questions about agentdb_pattern-store
Store a pattern directly via ReasoningBank controller 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 Write tool in the Ruflo MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Ruflo MCP server in PolicyLayer and add a rule for agentdb_pattern-store: 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_pattern-store is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the agentdb_pattern-store 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_pattern-store. 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_pattern-store 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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