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BACH-AI-Tools

Deezer 1 MCP Server

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 single tool 'api_endpoint' has a distinct and clear purpose by itself.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'api_endpoint' follows a clear and consistent pattern with no deviations to evaluate.

    Tool Count2/5

    A single tool is too few for a server named 'Deezer 1 MCP Server', which suggests a music streaming service domain. This minimal set severely limits functionality and likely indicates an incomplete implementation.

    Completeness1/5

    The tool set is severely incomplete for a Deezer music API server. With only one generic tool for adding endpoints, there are no specific operations for searching music, managing playlists, or accessing user data, which are core to the domain.

  • Average 1.9/5 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 is passing
  • This repository is licensed under MIT License.

  • 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

  • Behavior1/5

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

    No annotations are provided, so the description carries full burden for behavioral disclosure. The description reveals nothing about what the tool actually does behaviorally - whether it creates, validates, or modifies endpoints; what permissions are needed; whether it's read-only or mutating; what happens on success/failure; or any rate limits. It's essentially a placeholder instruction rather than a tool description.

    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, which is appropriately concise. However, it's not front-loaded with critical information - it reads more like an instruction to a human than a tool description for an AI agent. While brief, it wastes its limited space on vague phrasing rather than clear tool definition.

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

    Completeness1/5

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

    For a tool with no annotations, no output schema, and a vague purpose, this description is completely inadequate. It doesn't explain what the tool does, what behavior to expect, what the parameter means, or what results are returned. The description fails to provide the minimal context needed for an AI agent to understand and use this tool effectively.

    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% with one parameter documented as '参数' (parameter). The description adds no parameter-specific information beyond the schema. It doesn't explain what the 'param' parameter represents, what format it expects, or how it relates to 'endpoint information.' With high schema coverage, the baseline is 3, but the description adds zero value regarding parameters.

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

    Purpose2/5

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

    The description '请根据 RapidAPI 页面手动添加端点信息' translates to 'Please manually add endpoint information based on the RapidAPI page.' This is vague - it doesn't specify what action is being performed (create? update? validate?), what resource is being manipulated, or what 'endpoint information' means. While it mentions RapidAPI context, the purpose remains unclear and lacks a specific verb+resource combination.

    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?

    The description provides minimal guidance - it suggests this tool is used when working with RapidAPI endpoints, but gives no indication of when to use it versus alternatives, what triggers its use, or any prerequisites. With no sibling tools, differentiation isn't needed, but the description still lacks basic usage context beyond the RapidAPI reference.

    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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