Low Risk

kage_graph_insights

Return deterministic code graph intelligence: central files, dependency cycles, import communities, and short entry flows. Use to orient agents before broad architectural edits.

How to control kage_graph_insights ↓

What kage_graph_insights does on Kage

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

Low Risk

Why kage_graph_insights needs a policy

The tool purely retrieves and returns analytical information about the code graph (central files, cycles, communities, entry flows). It performs read-only analysis with no side effects, data modification, or execution of code. It is explicitly described as something to 'use to orient agents' before making changes, confirming its passive, informational nature.

From the tool's definition Return deterministic code graph intelligence: central files, dependency cycles, import communities, and short entry flows

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

How to control kage_graph_insights

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

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

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

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

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Questions about kage_graph_insights

What does the kage_graph_insights tool do? +

Return deterministic code graph intelligence: central files, dependency cycles, import communities, and short entry flows. Use to orient agents before broad architectural edits. It is categorised as a Read tool in the Kage MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on kage_graph_insights? +

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

What risk level is kage_graph_insights? +

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

Can I rate-limit kage_graph_insights? +

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

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

kage_graph_insights is provided by the Kage MCP server (@kage-core/kage-graph-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Kage tool call.

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

Free to start. No card required.

62 Kage tools catalogued and risk-classified — across an index of 43,000+ MCP servers.

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