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RAD Security

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by rad-security

mark_inbox_item_as_false_positive

Mark security alerts as false positives in RAD Security's inbox to reduce noise and focus on genuine threats by providing a reason for the classification.

Instructions

Mark an inbox item as a false positive with a reason

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inbox_item_idYesID of the inbox item to mark as false positive
valueNoWhether to mark the item as false positive (true) or not (false)
reasonYesReason for marking the item as false positive

Implementation Reference

  • Core handler function that executes the logic: constructs payload and makes PUT request to mark inbox item as false positive via the client.
    export async function markInboxItemAsFalsePositive(
      client: RadSecurityClient,
      inboxItemId: string,
      value: boolean = true,
      reason: string
    ): Promise<any> {
      const payload: Record<string, any> = { value, reason };
    
      return client.makeRequest(
        `/accounts/${client.getAccountId()}/inbox_items/${inboxItemId}/mark_false_positive`,
        {},
        { method: "PUT", body: payload }
      );
    }
  • Zod input schema defining parameters: inbox_item_id, value (default true), reason.
    export const MarkInboxItemAsFalsePositiveSchema = z.object({
      inbox_item_id: z.string().describe("ID of the inbox item to mark as false positive"),
      value: z.boolean().default(true).describe("Whether to mark the item as false positive (true) or not (false)"),
      reason: z.string().describe("Reason for marking the item as false positive"),
    });
  • src/index.ts:483-488 (registration)
    Tool registration in listTools handler: defines name, description, and input schema.
    name: "mark_inbox_item_as_false_positive",
    description:
      "Mark an inbox item as a false positive with a reason",
    inputSchema: zodToJsonSchema(
      inbox.MarkInboxItemAsFalsePositiveSchema
    ),
  • MCP CallToolRequest handler case: parses arguments with schema, calls core handler, returns JSON response.
    case "mark_inbox_item_as_false_positive": {
      const args = inbox.MarkInboxItemAsFalsePositiveSchema.parse(
        request.params.arguments
      );
      const response = await inbox.markInboxItemAsFalsePositive(
        client,
        args.inbox_item_id,
        args.value,
        args.reason
      );
      return {
        content: [
          { type: "text", text: JSON.stringify(response, null, 2) },
        ],
      };
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool performs a mutation ('mark'), implying it changes system state, but doesn't describe side effects (e.g., whether the item is removed from inbox, if the action is reversible, or permission requirements). For a mutation tool, this lack of behavioral context is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., side effects, permissions), usage context, or what the tool returns. Given the complexity of a state-changing operation, more information is needed to help the agent use it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all three parameters (inbox_item_id, value, reason). The description adds no additional parameter semantics beyond what's in the schema, such as format examples for 'reason' or clarification on the 'value' parameter's default behavior. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('mark as false positive') and target resource ('inbox item'), making the purpose immediately understandable. However, it doesn't differentiate this tool from any siblings since all other tools appear to be read-only queries or different operations, so it misses the opportunity to explicitly contrast with tools like 'update_security_finding_status' which might serve a similar status-update function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an inbox item ID from 'list_inbox_items' or 'get_inbox_item_details'), nor does it clarify scenarios where marking as false positive is appropriate versus other status updates. This leaves the agent without context for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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