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alexherbaly

upservice-mcp

by alexherbaly

upservice_update_task_worklog

Record actual effort in minutes for a task to update worklog and track time spent.

Instructions

Log actual effort (worklog) spent on a task, in minutes.

Args: params (UpdateTaskWorklogInput): task_id (int), value (int, minutes of actual effort)

Returns: str: JSON of the updated task/worklog.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false, confirming mutation but no destruction. However, the description does not add behavioral context beyond what annotations already convey. It fails to disclose whether the worklog is appended or overwritten, any required permissions, or side effects like changing task status. With annotations present, the description should provide additional behavioral nuance but does not.

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 extremely concise: a single-sentence summary followed by a structured Args/Returns block. There is no fluff, and every sentence provides necessary information. It is front-loaded with the core purpose, making it easy for an agent to quickly understand the tool's function.

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 that the tool is a mutation (write operation) and has an output schema, the description mentions the return type ('JSON of the updated task/worklog') which is adequate. However, it does not explain whether the worklog is appended or replaces existing entries, nor does it describe any constraints such as maximum minutes. For a mutation tool, this missing behavioral detail reduces completeness.

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 includes an explicit Args section listing both parameters (task_id, value) and their types, with value described as 'minutes of actual effort'. The input schema also provides descriptions for each parameter. Since the description clarifies the parameters beyond the schema—especially the unit for value—it adds value. Schema coverage is effectively 100% because both parameters have descriptions, so baseline is 3, and the description's extra context pushes it to 4.

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 'Log' and the resource 'actual effort (worklog) spent on a task', distinguishing it from sibling tools like upservice_update_task which handle general task updates. The specific reference to minutes and the inclusion of task_id and value parameters confirm the tool's focused purpose.

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?

No guidance is provided on when to use this tool versus alternatives such as upservice_get_task or upservice_update_task. It does not mention prerequisites (e.g., task must exist), nor does it specify if the worklog is appended or replaced. The description lacks any when-to-use or when-not-to-use context.

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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