Returns a detailed explanation of LabelHead's three-dimensional artist scoring methodology. Use this when you need to understand how composite scores are calculated, what each dimension measures, and how to interpret momentum labels.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It states the tool returns a 'detailed explanation' but does not specify the format or structure of the output, which is somewhat vague for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no redundancy. Every word serves a purpose, and the main action is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description perfectly covers what the tool does and when to use it. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema description coverage is 100% (trivially). The description adds meaningful context about what the explanation covers (composite scores, dimensions, momentum labels), exceeding the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a detailed explanation of LabelHead's three-dimensional artist scoring methodology, specifying composite scores, dimensions, and momentum labels. This distinguishes it from siblings like check_artist_momentum and get_trending_artists.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this when you need to understand...' providing clear context. However, it does not specify when not to use it or mention alternatives, which would strengthen the guidelines.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool serves a distinct purpose: individual lookup, methodology explanation, and trending list. No overlap exists.
All tool names follow a consistent verb_noun pattern in snake_case (check_, get_, get_).
With only 3 tools, the server is minimal but covers the core functionality for artist momentum queries and methodology explanation.
The tool set covers the main use cases (lookup, list, explain), though missing comparison or historical trend tools is a minor gap.