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oh-my-mcp

oh-my-mcp

by quyansiyuanwang
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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subagent_parallel

Execute multiple AI subtasks in parallel and aggregate their results. Break complex problems into independent subtasks for concurrent processing.

Instructions

Execute multiple AI subtasks in parallel with result aggregation.

Coordinates concurrent calls to different AI models and aggregates results. Useful for breaking complex problems into independent subtasks.

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": {
      "tasks": "<tasks>"
    },
    "name": "subagent_parallel"
  }
}

Input Schema

NameRequiredDescriptionDefault
tasksYesJSON string of task list. Each task: {provider, model, messages, max_tokens?, temperature?, name?}
max_workersNoMaximum concurrent tasks (default: 3, max: 10)

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries full burden. It discloses coordination of concurrent calls and result aggregation, which are key behaviors. However, it does not mention error handling, rate limits, or task independence beyond paras. The minimal disclosure earns a 3.

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?

Two concise sentences, front-loaded with the core action. Every sentence adds value without redundancy. Ideal structure for quick comprehension.

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?

Given the presence of an output schema (reducing return-value explanation burden) and schema coverage, the description covers parallelism and aggregation adequately. It lacks details on ordering or task independence, but for a coordination tool, it is sufficient. Score 4.

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 baseline is 3. The description adds minimal extra meaning: it mentions 'different AI models' which aligns with the provider/model in tasks. No further semantic detail beyond schema, so score 3.

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 tool's purpose: executing multiple AI subtasks in parallel and aggregating results. It uses a specific verb ('Execute') and resource ('multiple AI subtasks'), and distinguishes itself from sibling tools like subagent_call (single call) and subagent_conditional (conditional) by emphasizing parallelism and aggregation.

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?

The description provides clear usage context: 'useful for breaking complex problems into independent subtasks.' This implies when to use it, but does not explicitly state when not to use it or compare it to alternatives like subagent_call. Since siblings exist, it would benefit from direct differentiation, but the given context is adequate for a 4.

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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