appearances
Guest sit-ins / musician appearances, optionally filtered to one person.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| person | No |
good_honker
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It fails to explain output format, pagination, or what 'appearances' entails (e.g., date range, ordering).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short (12 words) and front-loaded, but it lacks structure (e.g., no sentence breaks). While concise, it sacrifices completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, no annotations, and 0% schema description coverage, the description is too minimal. It does not explain return values, default behavior, or how the limit parameter affects results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description adds meaning for the 'person' parameter ('filtered to one person') but provides no detail on 'limit' beyond its existence.
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 that the tool returns 'guest sit-ins / musician appearances' and mentions optional filtering by person. This distinguishes it from sibling tools like album_info or get_setlist.
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. The description does not indicate prerequisites, exclusions, or typical use cases.
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.