Waroom MCP

waroom_delete_service_label

Delete a label from a service by specifying the service name and label UUID. Streamline label management by removing unnecessary labels.

Instructions

特定のサービスのラベルを削除します。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "service_name": "<service_name>",
      "label_uuid": "<label_uuid>"
    },
    "name": "waroom_delete_service_label"
  }
}

Input Schema

Name	Required	Description	Default
`label_uuid`	Yes	削除するラベルのUUID
`service_name`	Yes	サービス名

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 18, 2025

TDQS

C2.9/5.0

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 action ('削除します' - delete) but doesn't cover critical aspects like whether this is a destructive operation (implied but not explicit), permission requirements, error handling, or what happens post-deletion (e.g., if it's irreversible). This is a significant gap for a mutation tool.

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 in Japanese that directly states the tool's purpose without unnecessary words. It's front-loaded and appropriately sized for a simple delete operation, earning its place with zero waste.

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?

Given the tool's complexity (a mutation operation with no annotations and no output schema), the description is incomplete. It lacks details on behavioral traits (e.g., destructiveness, auth needs), usage context, and expected outcomes, which are crucial for an agent to invoke it correctly and safely.

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%, with clear parameter descriptions in Japanese ('サービス名' - service name, '削除するラベルのUUID' - UUID of the label to delete). The description doesn't add extra meaning beyond the schema, such as format examples or constraints, but the schema is comprehensive, so a baseline score of 3 is appropriate.

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 verb ('削除します' - delete) and resource ('サービスのラベル' - service label), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'waroom_update_service_label' or 'waroom_get_service_labels', which would require more specific language about deletion versus modification or retrieval.

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?

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't mention prerequisites (e.g., needing an existing label), exclusions (e.g., not for bulk deletion), or refer to sibling tools like 'waroom_get_service_labels' for listing labels before deletion. This leaves the agent without context for proper tool selection.

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