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get_completed_tasks

Find completed tasks within specific projects and time ranges. Returns full task details (IDs, timestamps) for direct action.

Instructions

List tasks that have been completed within a given set of projects and time range.

All parameters are optional, but at least one is recommended to narrow the results. Returns complete task objects (id, projectId, completedTime, ...) so you can act on them directly without a second lookup.

Args: project_ids: List of project IDs to search across (use "inbox" for the inbox). start_date: ISO datetime WITH timezone offset, inclusive lower bound on completedTime (e.g., "2026-03-01T00:00:00+0800"). end_date: ISO datetime WITH timezone offset, inclusive upper bound on completedTime.

Examples: # Tasks completed in one project during March 2026 {"project_ids": ["abc123"], "start_date": "2026-03-01T00:00:00+0800", "end_date": "2026-03-31T23:59:59+0800"}

# All completed tasks across every project (no filters)
{}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_dateNo
start_dateNo
project_idsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description takes on the burden of behavioral disclosure. It reveals that the tool returns complete task objects so no second lookup is needed, that date bounds are inclusive, and that datetimes must include timezone offsets. It could go further on edge cases like no results, but it is solidly transparent.

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 well-organized with an intro, Args block, and Examples. Every sentence earns its place—optionality, return value, parameter meanings, and examples are all covered without redundancy.

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

Completeness5/5

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

Despite having an output schema, the description still adds valuable context about return value (complete objects) so the agent knows it can act directly. Parameter semantics are thorough, examples illustrate both filtered and unfiltered calls, and the tool's behavior is fully specified for its complexity.

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?

Schema coverage is 0%, so the description must and does provide full semantics for all three parameters. It explains project_ids accepts "inbox", start_date/end_date are ISO with timezone offsets and inclusive lower/upper bounds, and gives concrete examples that clarify usage.

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 verb "List" and the resource "completed tasks" within a project and time-range scope. This distinguishes it from sibling tools like query_tasks (which likely lists all tasks) and complete_tasks (which marks tasks complete).

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?

It provides clear context for when to use the tool (for completed tasks) and gives parameter guidance (at least one recommended to narrow results). However, it does not explicitly mention alternatives or exclude use cases, e.g., saying "use query_tasks for non-completed tasks."

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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