Medium Risk

type_definitions

_str_ (_optional_): Type definitions to be used for type checking. Default value is None.

Part of the Anirbanbasu Pymcp server.

type_definitions can modify Anirbanbasu Pymcp 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 type_definitions to create or modify resources in Anirbanbasu Pymcp. 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 type_definitions 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 Anirbanbasu Pymcp.

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

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

See the full Anirbanbasu Pymcp policy for all 17 tools.

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

_str_ (_optional_): Type definitions to be used for type checking. Default value is None.. It is categorised as a Write tool in the Anirbanbasu Pymcp MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on type_definitions? +

Register the Anirbanbasu Py MCP server in PolicyLayer and add a rule for type_definitions: 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 Anirbanbasu Pymcp. Nothing to install.

What risk level is type_definitions? +

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

Can I rate-limit type_definitions? +

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

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

type_definitions is provided by the Anirbanbasu Py MCP server (https://server.smithery.ai/@anirbanbasu/pymcp/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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

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