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todoist_update_task

Modify existing Todoist tasks by updating content, due dates, priorities, labels, or descriptions to keep your task management current.

Instructions

Update an existing task in Todoist.

Args: task_id: The ID of the task to update. content: Optional new task content. description: Optional new description. due_date: Optional new due date string. priority: Optional new priority (1-4). labels: Optional new list of label names.

Returns: A confirmation message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idYes
contentNo
descriptionNo
due_dateNo
priorityNo
labelsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 the tool updates tasks and returns a confirmation message, but lacks critical details: whether this requires authentication, what happens if optional fields are omitted (partial updates vs. overwrites), error conditions, or rate limits. For a mutation tool with zero annotation coverage, this is insufficient.

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 appropriately sized and front-loaded, starting with the core purpose. The 'Args' and 'Returns' sections are structured clearly, though the formatting could be more concise (e.g., bullet points instead of full sentences for parameters). Every sentence adds value, with no redundant information.

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's complexity (mutation with 6 parameters), lack of annotations, and presence of an output schema (implied by 'Returns'), the description is moderately complete. It covers parameters well but misses behavioral context like error handling or auth requirements. The output schema likely details the confirmation message, reducing the need for return value explanation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds significant value beyond the input schema, which has 0% description coverage. It explains each parameter's purpose (e.g., 'task_id: The ID of the task to update', 'priority: Optional new priority (1-4)'), clarifying semantics that aren't evident from schema types alone. This compensates well for the schema's lack of descriptions.

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 ('Update') and resource ('an existing task in Todoist'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from its sibling 'todoist_update_project', which updates projects rather than tasks, though this distinction is somewhat implied by the name and context.

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 like 'todoist_complete_task' or 'todoist_delete_task', nor does it mention prerequisites (e.g., needing an existing task ID). It simply states what the tool does without contextual usage information.

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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