Skip to main content
Glama
shiiman

multi-agent-mcp

by shiiman

assign_task_to_agent

Assign a task to an agent for parallel execution. Specify task ID, agent ID, and optionally a branch and worktree path.

Instructions

タスクをエージェントに割り当てる。

※ Admin のみ使用可能。

Args: task_id: タスクID agent_id: エージェントID branch: 作業ブランチ(オプション) worktree_path: worktreeパス(オプション) caller_agent_id: 呼び出し元エージェントID(ロールチェック用)

Returns: 割り当て結果(success, task_id, agent_id, message または error)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
branchNo
task_idYes
agent_idYes
worktree_pathNo
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?

With no annotations provided, the description carries the burden of behavioral disclosure. It mentions an Admin-only restriction and notes that 'caller_agent_id' is used for role checking. However, it does not disclose whether the tool is idempotent, what happens if the agent already has a task, or potential side effects.

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 concise: a single-line purpose, an admin note, and structured Arg/Returns sections. It is front-loaded and efficient, though the Args section repeats parameter names without elaboration.

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

Completeness3/5

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

Given the tool has 5 parameters and no annotations, the description provides the basic operation and return format. However, it does not explain how optional parameters impact behavior, nor does it help differentiate from many related task-management sibling tools. The output schema exists but is not described, though that is acceptable per rules.

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 0%, so the description must add meaning. It lists parameters with brief labels like 'タスクID' and 'エージェントID', but these are essentially the parameter names repeated. Optional parameters 'branch' and 'worktree_path' are described as 'optional' without explaining their purpose. This adds minimal value over 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 verb ('assign') and resource ('task to an agent'), making the tool's purpose unambiguous. It is distinct from sibling tools like 'auto_assign_tasks' which imply automated assignment, but the description does not explicitly differentiate them.

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 specifies that only Admin can use this tool, which is a clear prerequisite. However, it does not provide guidance on when to use this tool over alternatives like 'enqueue_task' or 'auto_assign_tasks', leaving the agent to infer appropriate usage from context.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/shiiman/multi-agent-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server