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shengchao73

MCP-Suno

by shengchao73
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    generate_lyrics and generate_music have clearly distinct outputs: one returns text (lyrics), the other returns audio/video (music). Their names and descriptions make the separation unambiguous.

    Naming Consistency5/5

    Both tools follow the exact same 'generate_<noun>' pattern, using snake_case consistently. This creates a predictable and coherent naming convention.

    Tool Count3/5

    With only 2 tools, the server feels thin for a music generation service. While the tools cover core generation, the count is borderline and would benefit from additional management or status tools.

    Completeness2/5

    The server only offers generation, lacking any way to list, retrieve, or manage past generations. For a realistic workflow, agents need status checks or history access, making the surface significantly incomplete.

  • Average 4.1/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 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

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  • This repository includes a README.md file.

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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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose the return value ('generated lyrics with title'), which is a positive trait, but it does not elaborate on output length, style constraints, or other behavioral nuances. It provides some transparency but not a comprehensive account.

    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 concise: two short sentences that lead with the core action and then state the return value. There is no superfluous text, making it well-structured and easy to parse.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a simple tool with one fully documented parameter and no output schema, the description covers the essential behavior (generates lyrics) and return format (with title). It is adequately complete for an agent to know what the tool does and what to expect, though it omits minor limitations or stylistic details.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema fully documents the single 'prompt' parameter with 100% coverage and includes examples. The description's phrasing ('based on a description or theme') merely paraphrases the schema's description and adds no additional semantic value, so the baseline of 3 applies.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's action (generate) and resource (lyrics), and specifies the input as 'a description or theme.' It differentiates from the sibling tool 'generate_music' by focusing on lyrics rather than music, 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 Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies when to use the tool—when lyrics are needed—but does not explicitly state when to prefer it over the sibling 'generate_music' or provide any exclusions or alternative guidance. The usage context is inferred rather than spelled out.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden. It discloses the output format (audio/video/cover URLs) and the special instrumental behavior. While it doesn't mention potential limitations or side effects, it covers the essential behavioral aspects for a generation tool.

    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, well-structured sentence that front-loads the main purpose and includes a critical usage tip. No redundant information; every word earns its place.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Despite no output schema, the description explicitly names the returned fields (audio URL, video URL, cover image), which is essential context. It also covers key parameter relationships. Minor gap: no mention of model version behavior or when to use vs. generate_lyrics, but overall adequate for the tool's complexity.

    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?

    Schema coverage is 100%, so baseline is 3. The description adds value by linking prompt and make_instrumental (instrumental mode requires empty prompt), and clarifying the inspiration mode for gpt_description_prompt. This goes beyond the individual schema descriptions.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states 'Generate custom music' and specifies return values (audio URL, video URL, cover image). This distinguishes it from the sibling tool generate_lyrics, which focuses on lyrics generation, 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 Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides clear usage context, especially for instrumental mode (leave prompt empty, set make_instrumental to true). However, it does not explicitly contrast with generate_lyrics or specify when to choose this tool over alternatives, so it lacks explicit exclusions.

    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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