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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    The three tools have clearly distinct purposes: cut_youtube_video submits a job, get_cut_status polls a job, and get_account_balance checks the API account. No overlaps or ambiguous boundaries between them.

    Naming Consistency4/5

    All tools follow a verb_object pattern (cut_youtube_video, get_cut_status, get_account_balance). However, cut_youtube_video uses 'cut' while the others use 'get', which is a minor inconsistency but not confusing.

    Tool Count5/5

    With 3 tools, the surface is tightly scoped for a video clipping service: submitting a cut job, polling its status, and checking account balance are each necessary and there is no bloat.

    Completeness4/5

    The core workflow (submit cut, poll status, retrieve download URL, check credits) is covered. Obvious gaps include no way to cancel/delete a job or list all jobs, but agents can work around these by waiting for job completion and tracking ids; thus minor gaps only.

  • Average 4.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 3 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

  • 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 adds useful context by specifying 'spendable' credit and 'hourly cap', but doesn't explicitly confirm the call is side-effect-free, discuss error scenarios, or note whether it hits an external service. For a no-parameter getter this is adequate but not exceptional.

    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?

    A single, front-loaded sentence that names the verb and both return values with zero wasted words. 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?

    For a zero-parameter, no-output-schema tool, the description covers the essential ground: what is returned and for whom. The only minor omission is specifics about the return format/units of the balance, but given the tool's simplicity and the clear sibling context, nothing critical is missing.

    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?

    With 0 parameters, baseline is 4. The description does clarify that the balance is for 'the API account' (not a passed-in user), which adds meaningful semantic context about whose balance this is. The schema is empty so there is nothing else to document.

    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 uses a specific verb ('Return') and names the exact resource ('API account's spendable credit balance and hourly cap'). It clearly distinguishes itself from the sibling tools (cut_youtube_video, get_cut_status), which are entirely different domains.

    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 its usage through context—it's the only read-only account-balance tool among the siblings. However, it never explicitly explains when to choose this over alternatives or mentions prerequisites like API key configuration. The differentiation from siblings is not explicit but is strongly implied.

    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?

    No annotations are provided, so the description carries the burden. It discloses the return states and the conditional download_url, which is good. However, it doesn't mention potential rate limits, error handling, or whether the job status is final, but for a simple status check this is adequate.

    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, front-loaded with the primary purpose, and includes necessary details without fluff. Every sentence adds value.

    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 status-check tool with one parameter and no output schema, the description is complete. It covers the states, the download_url condition, and the usage context. Minor gaps like error handling are not critical for this 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 coverage is 100% and the parameter is well-described in the schema. The description adds context by explaining the job_id is returned by cut_youtube_video, which is helpful but not essential beyond the schema.

    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 checks a cut job's status by its id, lists the possible states, and mentions the download_url condition. It distinguishes itself from siblings by referencing cut_youtube_video and the polling use case.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

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

    Explicitly states when to use this tool: to poll a job started with cut_youtube_video(wait=false) or one that timed out. This provides clear context and differentiates from the sibling tools.

    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?

    The description provides substantial behavioral details: default polling, timeout behavior, pricing, failure billing, stream-copy speed trade-offs, and mode-specific constraints. It does not cover error responses or rate limits, but given the scope, it is thoroughly transparent.

    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 a single block of text but is organized logically, starting with the core action and then layering pricing and behavioral details. It is not overly verbose given the complexity, though it could be broken into clearer paragraphs.

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

    Completeness5/5

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

    For a tool with 17 parameters, the description covers all necessary context: modes, polling, pricing, output format, and edge cases (e.g., fast mode, mutex with speed). Although there is no output schema, the return object is described (status with download_url), making the tool self-contained.

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

    Parameters5/5

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

    The input schema descriptions are extremely detailed, covering allowed values, defaults, constraints, and mutual exclusivities (e.g., fast vs. speed, clips vs. start/end). The overall description repeats key points. No parameter is left ambiguous.

    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 verb 'Cut' and the resource 'YouTube video', and explains the flow of submitting a job, polling, and returning a download URL. It is distinct from the sibling tools (get_cut_status and get_account_balance) which serve different purposes.

    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 implicitly indicates when to use the tool (for cutting videos) and describes the polling behavior and wait parameter, but does not explicitly contrast with the status-checking sibling tool. It is clear enough that an agent would know to use this for cutting rather than checking status.

    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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  • 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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