memory_build_context
AI agents call memory_build_context to retrieve information from Rekal without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The tool name suggests assembling or retrieving stored memory entries to build context, which is a read/query operation. However, the description is empty, reducing confidence. Given sibling tools like memory_related and conversation_threads that appear to be read operations, and the 'build' prefix suggesting aggregation rather than modification, Read is the most likely category.
From the tool's definition Tool name 'memory_build_context' and empty description. Based on naming convention and sibling tools (memory_delete, memory_prune suggest destructive ops; memory_related, memory_conflicts suggest read ops), 'build_context' likely aggregates/retrieves memory…
Documented attack patterns abuse exactly the kind of access memory_build_context gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Rekal, and nothing reaches the server without passing your rules. This is the rule we recommend for memory_build_context:
{
"version": "1",
"default": "deny",
"tools": {
"memory_build_context": {}
}
}memory_build_context is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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memory_build_context. It is categorised as a Read tool in the Rekal MCP Server, which means it retrieves data without modifying state.
Register the Rekal MCP server in PolicyLayer and add a rule for memory_build_context: 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 Rekal. Nothing to install.
memory_build_context 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 memory_build_context 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 memory_build_context. 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.
memory_build_context is provided by the Rekal MCP server (janbjorge/rekal). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Rekal, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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21 Rekal tools catalogued and risk-classified — across an index of 43,000+ MCP servers.