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jojoprison

mcp-music-forge

by jojoprison
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: probing a URL for downloadability, enqueuing a download job, and checking job status. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tools use a consistent verb_noun pattern in snake_case: enqueue_download, get_job_status, probe_url. This follows a predictable and clean naming convention.

    Tool Count5/5

    With 3 tools covering the core workflow (probe, enqueue, status), the count is well-scoped for a music download service. Not too few or too many.

    Completeness4/5

    The toolset covers the primary operations for downloading music from URLs, but lacks advanced features like cancelling jobs or listing all jobs. Minor gap but still functional.

  • Average 3/5 across 3 of 3 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 is failing

  • 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior2/5

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

    No annotations are provided, so the description is the sole source for behavioral disclosure. It mentions creating or deduplicating a job, implying state mutation, but does not detail side effects like whether jobs start processing immediately, or whether deduplication guarantees idempotency.

    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?

    A single sentence, highly concise, conveys core purpose without fluff. Could benefit from structured bullet points listing parameters or behavior, but current length is acceptable.

    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?

    The tool has an output schema but the description does not explain what it returns. For a complex tool with nested options, the description should clarify required parameters, default behaviors (like deduplication logic), and output format. Missing significant context.

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

    Parameters1/5

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

    Schema description coverage is 0%, so the description must explain parameters. The description does not mention the 'url' or 'options' parameters, leaving the agent to infer from names alone. The 'options' nested object with 6 properties is completely undocumented.

    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 clearly states the tool creates or deduplicates a job and enqueues it for processing. It distinguishes itself from sibling tools like get_job_status (read status) and probe_url (inspect URL). However, it lacks specificity on what 'dedupe' entails.

    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 usage guidelines provided. The description does not mention when to use this tool over alternatives, such as when to enqueue versus when to probe a URL or check job status.

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

  • Behavior2/5

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

    No annotations are provided, so the description must cover behavioral traits. It only states it returns data without disclosing whether it is read-only, safe to call repeatedly, or any side effects. This is a significant gap.

    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 concise sentence but is overly terse. It conveys the core action but lacks structure such as separate lines for status vs. artifact list, and could include more detail concisely.

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

    Completeness3/5

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

    Given the low complexity (1 required param, no enums) and presence of an output schema, the description is minimally adequate. However, it does not explain the relationship to other tools or the meaning of the status, which would help an agent.

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

    Parameters1/5

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

    Schema description coverage is 0%, and the description adds no meaning for the single parameter job_id. It does not explain the format, origin, or constraints of the job ID, leaving the agent without guidance.

    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 states the tool returns job status and artifact list, clearly indicating the verb and resources. However, it omits what kind of job or how status is represented, slightly reducing specificity. It is distinct from sibling tools enqueue_download and probe_url.

    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 to use this tool versus alternatives. No mention of prerequisites or context, such as whether a job ID from enqueue_download is needed.

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

  • Behavior3/5

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

    With no annotations, the description carries full burden. It describes core behavior but lacks details on side effects, idempotency, or network requests. Output schema exists but description doesn't mention read-only nature.

    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?

    Single, focused sentence with no redundancy. Front-loaded with key action (detect + check). Efficient for agent parsing.

    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 simple inputs and output schema, description covers core use. Could mention typical workflow with sibling tools. But overall adequate for a lightweight validation tool.

    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 0%, so description must compensate. It clarifies the url's role but doesn't specify format or constraints. Adds meaning but could be more precise.

    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 detects the provider for a URL and checks downloadability per provider rules. It distinguishes itself from sibling tools (enqueue_download and get_job_status) by focusing on pre-download validation.

    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 before downloading, but does not explicitly state when to use it vs alternatives or provide exclusions. Sibling names suggest context, but no direct guidance.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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