Manus MCP

create_task

Create AI tasks with custom prompts in Manus, specifying execution mode and optional attachments for automated processing.

Instructions

Create a new AI task in Manus. Returns task_id, task_title, task_url, and optionally a shareable link.

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": {
      "prompt": "<prompt>",
      "mode": "<mode>"
    },
    "name": "create_task"
  }
}

Input Schema

Name	Required	Description
`prompt`	Yes	The task prompt or instruction for the AI
`mode`	Yes	Execution mode: 'speed' for faster results, 'quality' for better accuracy
`attachments`	No	Optional attachments (files, URLs, etc.)
`connectors`	No	List of connector IDs to enable for this task (only pre-configured connectors)
`hide_in_task_list`	No	Whether to hide this task from the Manus webapp task list (default: false)
`create_shareable_link`	No	Whether to make the chat publicly accessible (default: false)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Feb 8, 2026

TDQS

B3.1/5.0

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 mentions that it 'Returns task_id, task_title, task_url, and optionally a shareable link,' which gives some output context, but lacks details on permissions, rate limits, side effects, or error handling for a creation 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 that front-loads the core action and key return values, with no wasted words. It effectively communicates the essential information in a compact form.

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 complexity of a creation tool with 6 parameters and no annotations or output schema, the description is minimally adequate. It covers the basic purpose and return values but lacks behavioral context and usage guidelines, leaving gaps for an AI agent to infer details.

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?

The input schema has 100% description coverage, so the schema fully documents all 6 parameters. The description adds no additional meaning beyond the schema, such as explaining interactions between parameters or usage examples, meeting the baseline for high schema coverage.

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 action ('Create a new AI task in Manus') and specifies the resource ('AI task'), which is distinct from sibling tools like create_webhook and delete_webhook. However, it doesn't explicitly differentiate from siblings beyond the resource type, missing a direct comparison.

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?

The description provides no guidance on when to use this tool versus alternatives, such as when to create a task versus a webhook, or any prerequisites like authentication needs. It only mentions the return values without context for usage decisions.

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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