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get_quality_trend

Analyze team output quality trends over time with per-dimension breakdown to identify improvement or decline.

Instructions

TRIGGER: Call this to check if the team's output quality is improving or declining over time. 📊 Quality trend dashboard with per-dimension breakdown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden. It states the tool returns a 'Quality trend dashboard with per-dimension breakdown,' implying a read-only operation (no side effects). However, it could be more explicit about whether any changes occur or what permissions are needed. Still, for a zero-parameter read tool, this is adequate.

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, consisting of two short sentences. The 'TRIGGER:' prefix front-loads the purpose. Every word adds value without redundancy. It is appropriately sized for the tool's simplicity.

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?

Given the tool has no parameters and an output schema exists, the description sufficiently covers the tool's behavior: it checks quality trends and provides a dashboard with per-dimension breakdown. There is no missing context for an agent to correctly invoke the tool. The presence of an output schema relieves the description from explaining return structure.

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?

There are zero parameters, so the description does not need to add parameter meaning. The input schema is empty with 100% coverage. Per baseline guidance, 0 parameters earns a score of 4. The description does not introduce any irrelevant parameter info.

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 'check' and the resource 'team's output quality' over time, and specifies the purpose: 'if the team's output quality is improving or declining.' It also distinguishes from the sibling 'get_prompt_quality_trend' by focusing on team output quality. The emoji and 'dashboard with per-dimension breakdown' enhance clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes 'TRIGGER:' implying a specific call condition, but it does not provide explicit when-to-use or when-not-to-use guidance compared to alternative tools. It only states a general scenario ('Call this to check...'). No exclusions or alternatives are mentioned.

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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