random_show
A random Goose show, with its full setlist.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
good_honker
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
B3.4/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavioral traits. It only states the outcome (returns a show) without mentioning that it is read-only, requires no authentication, or has no side effects.
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 a single, clear sentence with no extraneous information. It is appropriately sized for a simple tool with no parameters.
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?
While the description covers the core functionality, it lacks additional context such as the source of randomness, whether caching is involved, or how to interpret the setlist. This is adequate but minimal for a tool with no output schema.
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 input schema has no parameters, so schema coverage is 100%. The description adds no parameter details (none are needed), making this a baseline score of 4 for zero-parameter tools.
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 tool returns a random Goose show with its full setlist, effectively distinguishing it from siblings like get_setlist (which requires a specific show) and latest_show.
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?
No explicit when-to-use or when-not-to-use guidance is provided. The usage is implied by the name and description, but alternatives are not discussed.
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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TDQS
A3.5/5.0
Disambiguation5/5
Each tool targets a distinct aspect of the band's data: albums, appearances, setlists, jams, shows, songs, venues, etc. There is no overlap in functionality, making it easy for an agent to select the correct tool.
Naming Consistency5/5
All tool names follow the snake_case convention (e.g., album_info, get_setlist, song_stats). The naming is descriptive and consistently uses nouns or verb_noun patterns without mixing styles.
Tool Count5/5
With 14 tools, the server is well-scoped for a band discography/event database. Each tool serves a clear purpose and the count is balanced—neither too few nor excessive.
Completeness5/5
The tool set covers all key operations: searching shows, retrieving setlists, accessing song history and stats, managing albums, venues, jams, appearances, and even health checks. There are no obvious gaps in covering the band's data lifecycle.