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awslabs

Threat Modeling MCP Server

Official
by awslabs

add_component_to_zone

Add components to trust zones in your architecture. Supports single or batch operations by providing zone and component IDs, enabling efficient grouping for structured threat modeling.

Instructions

Add a component to a trust zone. Supports batch operations via the 'items' parameter.

This tool adds one or more components to trust zones in the system architecture. For single item: provide zone_id and component_id directly. For batch: provide a list of dicts in the 'items' parameter.

Args: ctx: MCP context for logging and error handling zone_id: ID of the trust zone (required for single item mode) component_id: ID of the component to add (required for single item mode) items: Optional list of zone-component pair dicts for batch operation

Returns: A confirmation message

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsNoOptional list of zone-component pairs to add in batch. Each dict should contain 'zone_id' and 'component_id'. When provided, individual parameters are ignored.
zone_idNoID of the trust zone (required for single item mode)
component_idNoID of the component to add (required for single item mode)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Despite no annotations, the description discloses key behavioral details: batch mode is available, items overrides individual parameters when provided, and returns a confirmation message. It does not mention idempotency or permission requirements, but the core side effects are covered.

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

Conciseness4/5

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

The description is well-structured with Args and Returns sections. A slight redundancy exists between the first sentence and the second paragraph, but overall it is concise and front-loaded with the core purpose.

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

Completeness4/5

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

For a tool with no annotations, the description adequately covers modes, parameters, and return value. It does not address edge cases like duplicate additions or error handling, but those are less critical for a simple add operation and output schema exists.

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

Parameters5/5

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

Schema coverage is 100% and the description adds meaningful semantics: it explains the relationship between items and individual parameters (items take precedence), and clarifies the required fields for each mode. This goes beyond the schema descriptions.

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

Purpose5/5

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

The description clearly states the action: 'Add a component to a trust zone' and immediately notes batch support. It distinguishes from siblings like remove_component_from_zone and add_trust_zone by specifying the resource and relationship.

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

Usage Guidelines4/5

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

Provides explicit usage modes for single ('provide zone_id and component_id directly') and batch ('provide a list of dicts in the items parameter'). It does not explicitly name alternatives or exclusion conditions, but the context is clear enough for a mutation tool.

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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