show_extras
Poster art, images, and external links attached to a show on a given date.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes |
good_honker
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.8/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It discloses the tool returns poster art, images, and external links, implying read-only behavior. However, it does not confirm whether changes are made or if authentication is needed, though for a read tool this is acceptable.
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 a single sentence with no fluff, achieving conciseness. However, it is borderline under-specified; a slightly longer description could include clarifying details without harming structure.
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?
For a tool with one parameter and no output schema, the description is minimal. It does not explain what 'extras' encompasses, whether multiple items are returned, or any ordering criteria. The agent lacks context to anticipate the response shape or handle edge cases.
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 coverage is 0%, so the description must add meaning. It mentions 'on a given date', linking the 'date' parameter to the purpose. But it provides no format, constraints, or additional context beyond the parameter name, leaving ambiguity (e.g., date format or range).
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 retrieves poster art, images, and external links for a show on a given date. The verb 'attached to a show' implicitly indicates retrieval. It distinguishes from siblings like 'get_setlist' or 'album_info' by specifying the type of data (extras).
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 like 'latest_show' or 'search_shows'. The description does not mention any prerequisites, limitations, or exclusions (e.g., only works for shows with date entry).
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.