Low Risk

read_memory

Read from your hierarchical long-term memory (.hmem).

Part of the hmem — Humanlike Memory for AI Agents server.

read_memory is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

SECURE HMEM — HUMANLIKE MEMORY FOR AI AGENTS →

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AI agents call read_memory to retrieve information from hmem — Humanlike Memory for AI Agents without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though read_memory only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "read_memory": {}
  }
}

See the full hmem — Humanlike Memory for AI Agents policy for all 23 tools.

Get this rule live on your own hmem — Humanlike Memory for AI Agents server in minutes. PolicyLayer enforces it on every call, before it runs.

ENFORCE ON MY HMEM — HUMANLIKE MEMORY FOR AI AGENTS →

View all 23 tools →

These attack patterns abuse exactly the kind of access read_memory gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so read_memory only ever does what you allow.

SECURE HMEM — HUMANLIKE MEMORY FOR AI AGENTS →

Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the read_memory tool do? +

Read from your hierarchical long-term memory (.hmem).. It is categorised as a Read tool in the hmem — Humanlike Memory for AI Agents MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on read_memory? +

Register the hmem — Humanlike Memory for AI Agents MCP server in PolicyLayer and add a rule for read_memory: 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 hmem — Humanlike Memory for AI Agents. Nothing to install.

What risk level is read_memory? +

read_memory is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit read_memory? +

Yes. Add a rate_limit block to the read_memory 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.

How do I block read_memory completely? +

Set action: deny in the PolicyLayer policy for read_memory. 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.

What MCP server provides read_memory? +

read_memory is provided by the hmem — Humanlike Memory for AI Agents MCP server (hmem-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every hmem — Humanlike Memory for AI Agents tool call.

Deterministic rules across all 23 hmem — Humanlike Memory for AI Agents tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

Free to start. No card required.

4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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