Medium Risk

workspace_save

Save calculation to persistent workspace (survives restarts). Examples: save_calculation("portfolio_return", "10000 * 1.07^5", 14025.52) save_calculation("circle_area", "pi * 5^2", 78.54)

Risk signalsAccepts freeform code/query input (expression)

Part of the Math MCP Learning server.

workspace_save can modify Math MCP Learning 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 workspace_save to create or modify resources in Math MCP Learning. 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 workspace_save 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 Math MCP Learning.

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

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

See the full Math MCP Learning policy for all 17 tools.

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These attack patterns abuse exactly the kind of access workspace_save 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 workspace_save 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 workspace_save tool do? +

Save calculation to persistent workspace (survives restarts). Examples: save_calculation("portfolio_return", "10000 * 1.07^5", 14025.52) save_calculation("circle_area", "pi * 5^2", 78.54). It is categorised as a Write tool in the Math MCP Learning MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on workspace_save? +

Register the Math MCP Learning MCP server in PolicyLayer and add a rule for workspace_save: 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 Math MCP Learning. Nothing to install.

What risk level is workspace_save? +

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

Can I rate-limit workspace_save? +

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

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

workspace_save is provided by the Math MCP Learning MCP server (pypi:math-mcp-learning-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Math MCP Learning tool call.

Deterministic rules across all 17 Math MCP Learning tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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