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lioensky

Suno-MCP

by lioensky
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'generate_music_suno' has a singular, well-defined purpose of generating music via the Suno API.

    Naming Consistency5/5

    The single tool name 'generate_music_suno' follows a clear verb_noun pattern, and there are no other tools to cause inconsistency. The naming is straightforward and descriptive.

    Tool Count2/5

    A single tool is too few for a server named 'Suno-MCP', which suggests a broader music generation domain. This minimal toolset feels thin and may limit functionality, as agents might expect additional operations like listing generated songs or managing settings.

    Completeness2/5

    The tool surface is severely incomplete for a music generation server. While 'generate_music_suno' handles creation, there are obvious gaps such as retrieving, updating, or deleting generated songs, and no tools for managing styles or user preferences, which could hinder agent workflows.

  • Average 3.7/5 across 1 of 1 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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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?

    With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool returns an audio URL, polling may take minutes, and it includes a specific HTML format for user convenience. However, it doesn't cover potential errors, rate limits, or authentication needs, leaving some gaps.

    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?

    The description is appropriately sized and front-loaded with the core purpose, followed by usage notes and output details. The HTML format section is lengthy but serves a practical purpose. Some sentences could be more concise, but overall it's efficient with minimal waste.

    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?

    Given the complexity (9 parameters, no output schema, no annotations), the description does a good job covering the tool's purpose, modes, and output handling. It lacks details on error cases or advanced usage scenarios, but for a generative tool with rich schema coverage, it's reasonably complete.

    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?

    Schema description coverage is 100%, so the schema already documents all 9 parameters thoroughly. The description adds minimal value by mentioning lyrics, style, and title for custom mode and description for inspiration mode, but doesn't provide additional syntax or format details beyond what the schema provides. Baseline 3 is appropriate when schema does the heavy lifting.

    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 'Generates a song using the Suno API' with specific verbs ('generates') and resources ('song'), and mentions two modes (custom and inspiration). However, it doesn't distinguish from any siblings since none exist, so it can't achieve a perfect 5 for sibling differentiation.

    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 usage by mentioning two modes (custom vs inspiration) and their required inputs, but it doesn't explicitly state when to choose one mode over the other or provide broader contextual guidance. No alternatives are mentioned, but since there are no sibling tools, this is less critical.

    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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