Medium Risk

discover_agents

Scan editor configs (Zed settings.json, JetBrains acp.json) for ACP agents. Returns agents with command/args/env and source. To import, add them to agent_servers in your mcacp.json and call reload_config.

Part of the Mcacp server.

discover_agents can modify Mcacp data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use discover_agents to create or modify resources in Mcacp. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call discover_agents repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Mcacp.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "discover_agents": {
      "limits": [
        {
          "counter": "discover_agents_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Mcacp policy for all 24 tools.

Get this rule live on your own Mcacp server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access discover_agents 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 discover_agents only ever does what you allow.

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Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the discover_agents tool do? +

Scan editor configs (Zed settings.json, JetBrains acp.json) for ACP agents. Returns agents with command/args/env and source. To import, add them to agent_servers in your mcacp.json and call reload_config.. It is categorised as a Write tool in the Mcacp MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on discover_agents? +

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

What risk level is discover_agents? +

discover_agents is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit discover_agents? +

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

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

discover_agents is provided by the Mcacp MCP server (mcacp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Mcacp tool call.

Deterministic rules across all 24 Mcacp tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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