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Server Quality Checklist

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  • Latest release: v1.0.5

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: check_usage for account info, download_clip for retrieving download URLs, generate_clips for creating new clips, get_clip for clip details, get_project_status for project progress, and list_projects for project overview. There is no overlap in functionality, making tool selection unambiguous.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern using snake_case: check_usage, download_clip, generate_clips, get_clip, get_project_status, list_projects. The naming is predictable and readable throughout the set.

    Tool Count5/5

    With 6 tools, the count is well-scoped for a video clip generation service. Each tool serves a specific role in the workflow, from project creation to status checking and clip retrieval, without being excessive or insufficient.

    Completeness4/5

    The toolset covers core workflows: generating clips, checking project status, listing projects, retrieving clip details, and downloading clips, with account usage monitoring. A minor gap is the lack of update or delete operations for projects or clips, but agents can work around this for most tasks.

  • Average 3.3/5 across 6 of 6 tools scored.

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

    • 0 of 1 community issues answered or closed 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

  • Behavior2/5

    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 states the tool retrieves details but doesn't cover critical aspects like authentication requirements, rate limits, error handling (e.g., invalid clip IDs), or response format. The mention of 'export status and URLs' hints at behavior but lacks depth, leaving gaps in understanding how the tool operates.

    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, efficient sentence that front-loads the core action ('Get details') and specifies key outputs ('export status and URLs') without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, making it appropriately sized and well-structured.

    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?

    Given the tool's complexity (a read operation with one parameter) and the absence of annotations and output schema, the description is incomplete. It lacks details on behavioral traits (e.g., permissions, errors), output structure, and usage context. While concise, it doesn't provide enough information for an agent to fully understand how to invoke and interpret results 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?

    The input schema has 100% description coverage, with the single parameter 'clip_id' documented as 'The clip ID'. The description adds no additional meaning beyond this, such as format examples or sourcing context. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract from the schema's clarity.

    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's purpose with a specific verb ('Get') and resource ('clip'), specifying what details are retrieved ('export status and URLs'). It distinguishes itself from siblings like 'download_clip' by focusing on metadata retrieval rather than file download. However, it doesn't explicitly differentiate from other potential read operations like 'list_projects' beyond the resource type.

    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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a valid clip ID), when not to use it (e.g., for bulk operations), or direct comparisons to siblings like 'list_projects' for overviews versus detailed views. Usage is implied only by the tool's name and description.

    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 carries the full burden. It mentions listing 'recent' projects and their status, implying a read-only operation, but fails to disclose key behavioral traits like pagination, default sorting, rate limits, or authentication needs. This leaves significant gaps for an agent to understand how the tool behaves.

    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, efficient sentence that front-loads the core action and resource. There's no wasted text, making it easy to parse, though it could be slightly more structured by explicitly mentioning optional parameters or output format.

    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?

    Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., list structure, fields included), behavioral details like ordering or limits, or how it differs from siblings. For a tool with two parameters and no structured output, more context is needed to be fully helpful.

    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 input schema already documents both parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as explaining the context of 'recent' or how status filtering interacts with listing. Baseline 3 is appropriate when the schema handles parameter documentation adequately.

    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 verb 'List' and resource 'recent clip generation projects with their status', making the purpose specific and understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'get_project_status' which might retrieve status for a single project, leaving room for potential confusion.

    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 no guidance on when to use this tool versus alternatives like 'get_project_status' or 'generate_clips'. It lacks context about prerequisites, such as whether projects need to exist first, or exclusions, making it minimally helpful for decision-making.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool checks credits and plan, implying a read-only operation, but doesn't specify whether this requires authentication, has rate limits, returns real-time or cached data, or what format the output takes (e.g., JSON with specific fields). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

    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, efficient sentence that front-loads the core purpose ('Check your remaining credits and current plan') with zero waste. Every word earns its place by specifying what is checked, making it easy for an agent to quickly understand the tool's function without parsing extraneous information.

    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 tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states the purpose clearly but lacks context about authentication, output format, or usage scenarios. Without annotations or an output schema, the agent must guess behavioral details, making this incomplete for reliable tool selection and invocation.

    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?

    The input schema has 0 parameters with 100% coverage (since there are none), so the baseline is 4. The description appropriately doesn't discuss parameters, as none exist, and doesn't add unnecessary details. It focuses on the tool's purpose without parameter-related distractions.

    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 what the tool does ('Check your remaining credits and current plan') with a specific verb ('Check') and resource ('credits and current plan'). It distinguishes itself from sibling tools like 'download_clip' or 'list_projects' by focusing on account/usage information rather than media or project management. However, it doesn't explicitly differentiate from all siblings (e.g., 'get_project_status' might also involve status checking).

    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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (like authentication needs), appropriate contexts (e.g., before generating clips to avoid credit exhaustion), or relationships with sibling tools (e.g., whether 'get_project_status' might overlap with plan information). The agent must infer usage from the purpose alone.

    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 carries the full burden of behavioral disclosure. It mentions the tool returns 'current processing step and any generated clips', which adds some context about output behavior. However, it fails to disclose critical traits such as whether this is a read-only operation, error handling for invalid IDs, rate limits, or authentication needs, leaving significant gaps for an agent.

    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 concise and front-loaded, consisting of two sentences that directly state the tool's purpose and return values without unnecessary details. Every sentence contributes to understanding, though it could be slightly more structured by separating usage hints from output details.

    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 tool's moderate complexity (single parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and output hints but lacks details on behavioral aspects like error cases or performance, which are important for an agent to use it correctly in a real-world context.

    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 has 100% description coverage, with the parameter 'project_id' well-documented in the schema. The description adds minimal value by referencing 'project_id returned from generate_clips', which provides slight contextual meaning but doesn't elaborate on format or constraints beyond what the schema already states. This meets the baseline for high schema coverage.

    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's purpose with a specific verb ('Check') and resource ('clip generation project'), and it distinguishes the tool from siblings by focusing on status retrieval rather than generation, listing, or downloading. However, it doesn't explicitly differentiate from 'list_projects' in terms of scope (single vs. multiple projects).

    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 referencing 'project_id returned from generate_clips', suggesting it should be used after clip generation. However, it lacks explicit guidance on when to use this tool versus alternatives like 'list_projects' for overviews or 'get_clip' for specific clip details, and it doesn't mention prerequisites or exclusions.

    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 provided, the description carries the full burden. It discloses that the tool processes videos with AI, returns clip details with download URLs, and mentions polling behavior via 'wait_for_completion'. However, it lacks details on rate limits, authentication needs, or error handling.

    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 front-loaded with the core purpose, followed by key behavioral details. Each sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

    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 tool's complexity (AI processing, async behavior) and lack of output schema, the description adequately covers the purpose, process, and output. It could improve by detailing error cases or response formats, but it provides sufficient context for basic usage.

    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 parameters thoroughly. The description adds context about AI processing and output formats but does not provide additional semantic details beyond what the schema specifies for each parameter.

    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 specific action ('Generate short-form video clips'), resource ('from a YouTube URL'), and purpose ('ready for TikTok, Shorts, and Reels'). It distinguishes from siblings like 'download_clip' or 'get_project_status' by focusing on creation rather than retrieval or download.

    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 for creating clips from YouTube videos but does not explicitly state when to use this tool versus alternatives like 'list_projects' or 'get_project_status'. It mentions the tool's function but lacks guidance on prerequisites or exclusions.

    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 and adds valuable behavioral context: it discloses that the tool automatically handles export if needed, waits for readiness, and returns a download link. This covers key operational traits like async processing and output format. It doesn't mention permissions, rate limits, or error handling, but provides solid foundational behavior.

    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 two concise sentences that are front-loaded with the core purpose and efficiently cover key behavioral aspects (export handling, waiting, return value). Every sentence adds value without redundancy, making it easy to scan and understand quickly.

    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 no annotations and no output schema, the description does well by explaining the tool's behavior (export handling, waiting, returning a link) and purpose. It covers the essential context for a download tool with async processing. However, it lacks details on response format (e.g., URL structure) or error cases, leaving minor gaps in completeness.

    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 fully documents both parameters (clip_id and quality with enum values). The description doesn't add any parameter-specific details beyond what the schema provides, such as explaining clip_id format or quality implications. Baseline 3 is appropriate as the schema handles the heavy lifting.

    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 purpose with specific verbs ('Get a download URL', 'Automatically handles export', 'waits until the clip is ready', 'returns the download link') and identifies the resource ('clip'). It distinguishes itself from sibling tools like 'get_clip' (likely retrieves metadata) by focusing on download URL generation with export handling.

    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 when a download URL for a clip is needed, including automatic export handling if required. However, it doesn't explicitly state when to use this tool versus alternatives (e.g., 'get_clip' for metadata only) or mention prerequisites like clip existence. The guidance is functional but lacks explicit comparison or 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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