ping
Health check and a manifest of what good_honker can do.
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
B3/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the burden. It says 'health check and a manifest' but does not detail what the manifest contains, any side effects (none expected), or the response format. The description is vague about the 'manifest' part.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence conveying core purpose with no fluff. However, it could be slightly more structured (e.g., 'Health check endpoint returning a manifest of available tools') without adding length.
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?
No output schema exists, so description should explain the return value. 'Health check and a manifest' hints at a status and list but does not specify format (e.g., JSON with 'status' and 'tools' fields). The tool is simple, but the description is incomplete for an agent to understand the response.
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?
No parameters exist, and schema coverage is 100% (empty object). Baseline is 4 per instruction. The description adds no parameter information, which is acceptable because no parameters need explanation.
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?
Description states 'Health check and a manifest of what good_honker can do.' This clearly identifies the tool as a health check for the server and a listing of capabilities. It distinguishes from sibling tools which are all Phish-related data queries.
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 on when or why to use this tool versus alternatives. Although no sibling tool serves a similar purpose, the description does not explicitly say 'use to verify connectivity' or mention any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
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.