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shiiman

multi-agent-mcp

by shiiman

initialize_agent

Initialize an agent by loading a role template and launching an AI CLI terminal, enabling role-based interactions.

Instructions

エージェントを初期化し、ロールテンプレートを渡して AI CLI を起動する。

create_agent で作成されたエージェントに対して、roles/ テンプレートを 初期プロンプトとして渡し、ターミナルで AI CLI を起動する。

※ Owner と Admin のみ使用可能。

Args: agent_id: 初期化するエージェントID prompt_type: プロンプトタイプ - "auto": roles/ テンプレートを自動読み込み(デフォルト) - "custom": custom_prompt をそのまま使用 - "file": custom_prompt をファイルパスとして読み込み custom_prompt: カスタムプロンプト(prompt_type が "custom" または "file" の場合) terminal: ターミナルアプリ(auto/ghostty/iterm2/terminal) caller_agent_id: 呼び出し元エージェントID(必須)

Returns: 初期化結果(success, agent_id, cli, prompt_source, message)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYes
terminalNoauto
prompt_typeNoauto
custom_promptNo
caller_agent_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses that the tool launches a terminal CLI and requires Owner/Admin role. However, it does not mention side effects, reversibility, or potential environment requirements. More behavioral context would be beneficial.

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 a clear one-line summary, detail paragraph, and formatted Args list. It is front-loaded. Minor redundancy: the first two sentences convey similar information.

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?

The description covers purpose, constraints, parameters, and return structure (including key fields). Given moderate complexity and output schema presence, it is fairly complete. Could add error scenarios or more explicit prerequisites.

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?

The input schema has no descriptions (0% coverage), but the description's Args block fully explains each parameter's purpose, valid values, defaults, and dependencies (e.g., custom_prompt only relevant for custom/file types). This adds significant meaning beyond the schema.

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 tool initializes an agent and launches AI CLI with a role template. It mentions the prerequisite (agent created with create_agent) and the action. However, it does not explicitly differentiate from sibling tools like create_agent or terminate_agent, though the purpose is distinct.

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

Usage Guidelines3/5

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

The description provides usage context: only Owner and Admin can use, and it details prompt_type options (auto, custom, file). It implies usage after create_agent but does not explicitly state when not to use this tool or compare with alternatives. No exclusion criteria.

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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