Medium Risk

save_plays

Plans de sauvetage clients — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Kyriba — Plan sauvetage 30j · ARR €11.988 · Champion parti · Script 6 actions · 3 concessions. Inputs are validated server-side — send the documented case fields.

Risk signalsHigh parameter count (13 properties)

Part of the Gapup Mcp server.

save_plays can modify Gapup Mcp 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 save_plays to create or modify resources in Gapup Mcp. 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 save_plays 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 Gapup Mcp.

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

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

See the full Gapup Mcp policy for all 271 tools.

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

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so save_plays 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 save_plays tool do? +

Plans de sauvetage clients — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Kyriba — Plan sauvetage 30j · ARR €11.988 · Champion parti · Script 6 actions · 3 concessions. Inputs are validated server-side — send the documented case fields.. It is categorised as a Write tool in the Gapup Mcp MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on save_plays? +

Register the Gapup MCP server in PolicyLayer and add a rule for save_plays: 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 Gapup Mcp. Nothing to install.

What risk level is save_plays? +

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

Can I rate-limit save_plays? +

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

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

save_plays is provided by the Gapup MCP server (https://mcp.gapup.io/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Gapup Mcp tool call.

Deterministic rules across all 271 Gapup Mcp tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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