maker_hint

Request a scaffolded hint for the current activity. Output is filtered (reading-level / blocklist / length) and rate-limited. On filter failure, returns a canned lesson hint. In the

ServerCrow
CategoryRead

How to control maker_hint ↓

WHAT IT DOES

What maker_hint does on Crow

AI agents call maker_hint to retrieve information from Crow without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

THE RISK

Low Risk

Why maker_hint needs a policy

This tool performs a read-only operation that retrieves hint information filtered by reading level, blocklist, and length constraints. There are no side effects, data modifications, or external operations triggered. The rate-limiting and filtering are safety measures that further confirm this is a passive retrieval operation with negligible risk.

From the tool's definition 'Request a scaffolded hint for the current activity. Output is filtered...and rate-limited.' The tool retrieves and returns hint content without modifying, deleting, or executing operations.

Documented attack patterns abuse exactly the kind of access maker_hint gives an agent:

POLICY

How to control maker_hint

PolicyLayer is an MCP gateway — it sits between your AI agents and Crow, and nothing reaches the server without passing your rules. This is the rule we recommend for maker_hint:

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "maker_hint": {}
  }
}

maker_hint is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.

Create a free account and register Crow — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

CAP THIS TOOL →

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EXPLORE

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Go deeper

The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Data exfiltration → How read access becomes an exfiltration channel when chained with untrusted content.
MCP token cost → What connecting this server costs in context-window tokens on every request.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about maker_hint

What does the maker_hint tool do? +

Request a scaffolded hint for the current activity. Output is filtered (reading-level / blocklist / length) and rate-limited. On filter failure, returns a canned lesson hint. In the. It is categorised as a Read tool in the Crow MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on maker_hint? +

Register the Crow MCP server in PolicyLayer and add a rule for maker_hint: 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 Crow. Nothing to install.

What risk level is maker_hint? +

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

Can I rate-limit maker_hint? +

Yes. Add a rate_limit block to the maker_hint 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 maker_hint completely? +

Set action: deny in the PolicyLayer policy for maker_hint. 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 maker_hint? +

maker_hint is provided by the Crow MCP server (kh0pper/crow). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Crow tool call.

Start from Crow, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

CHECK YOUR STACK →

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