gequbao
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
The two tools serve clearly distinct purposes: search for songs and retrieve a playback URL. There is no overlap or ambiguity between them.
Naming Consistency5/5Both tool names follow a consistent snake_case and verb_noun pattern: search_with_full_title and get_music_url. No naming conflicts or style mixing.
Tool Count3/5With only 2 tools, the server is minimal. While it covers a basic search-to-play workflow, the number feels slightly thin for a music service. However, it is not extreme and matches the narrow scope.
Completeness4/5The core workflow of searching for songs and retrieving audio URLs is covered. Minor gaps exist, such as no separate tool for song metadata only, but agents can work around by using the search result data.
Average 4.8/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It details the return format (ok, enriched, songs) and error cases, and explains the 'enrich' parameter's effect. However, it does not mention any potential side effects or rate limits, which are minor omissions for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections for usage, parameters, return values, and constraints. Each sentence serves a purpose, though it is slightly verbose with emojis and formatting. Still, it is efficient for its content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description fully defines the return structure for both success and failure. All parameters are explained, usage constraints are given, and the tool's behavior is fully covered. No gaps remain for the agent to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully explain parameters. It does: 'keyword' is search term, 'limit' is count with default 5, 'enrich' is boolean for full title completion with default false. This adds significant meaning beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a music search tool and provides a specific verb ('搜索') and resource ('音乐'). It distinguishes itself from the sibling tool 'get_music_url' which is likely for retrieving URLs, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is given: suggests increasing 'limit' or setting 'enrich=True' if results are unsatisfactory, and explicitly prohibits calling playback tools during search. This provides clear when-to-use and when-not-to-use instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description fully discloses behavior: it takes a detail_url, returns success/failure structures, and outlines constraints. It's transparent about not being a search or batch tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections and emojis for readability, but some emojis and formatting are unnecessary. It is clear and efficient, though slightly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 1-parameter tool, the description covers purpose, usage, constraints, and return formats (both success and failure). No output schema exists, but the description provides explicit structures.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description explains the sole parameter 'detail_url' as a string and shows how it's used in the return example. It adds full meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The title and description clearly state the tool's purpose: retrieving a music playback URL for a single song. It distinguishes from the sibling tool 'search_with_full_title' by focusing on retrieval after identification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage constraints: only call after confirming target song, only one song per call, no batch calls, and not during search phase. This guides appropriate use vs alternatives.
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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