Skip to main content
Glama

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: cancel_task stops tasks, download_video initiates downloads, get_task_status checks progress, get_video_info retrieves metadata, and list_tasks enumerates tasks. An agent can easily distinguish between these operations.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (e.g., cancel_task, download_video, get_task_status), using snake_case throughout. The naming is predictable and readable across the entire set.

    Tool Count5/5

    With 5 tools, this server is well-scoped for video fetching, covering core operations like downloading, status checking, task management, and info retrieval. Each tool earns its place without being excessive or insufficient.

    Completeness4/5

    The toolset covers the essential lifecycle of video fetching: initiating downloads (download_video), monitoring (get_task_status, list_tasks), and metadata retrieval (get_video_info). A minor gap is the lack of a tool for modifying or resuming tasks, but agents can work around this with the existing tools.

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

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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 action ('get') but doesn't explain what 'status' entails (e.g., progress percentage, success/failure, error details), response format, or any side effects like rate limits or authentication needs, leaving significant gaps.

    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 phrase ('获取下载任务状态') that directly states the purpose without unnecessary words. It's front-loaded and appropriately sized for a simple tool, with zero waste.

    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 status-checking operation with no annotations and no output schema), the description is incomplete. It doesn't cover what the status includes, how to interpret results, or error handling, making it inadequate for effective agent use despite the concise phrasing.

    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 'taskId' documented as '任务ID' (task ID). The description doesn't add meaning beyond this, such as format examples or where to obtain the ID, but the schema provides adequate baseline information, justifying a score of 3.

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

    Purpose3/5

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

    The description '获取下载任务状态' (Get download task status) clearly states the verb ('get') and resource ('download task status'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'list_tasks' or 'get_video_info', leaving ambiguity about scope and specificity.

    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 is provided on when to use this tool versus alternatives. The description lacks context such as prerequisites (e.g., needing a taskId from another operation) or comparisons to siblings like 'list_tasks' for broader status overviews, leaving usage unclear.

    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 full burden. It states the action (cancel) but lacks behavioral details: whether cancellation is reversible, if it requires specific permissions, what happens to partial downloads, or error conditions. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

    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 phrase '取消下载任务', which is extremely concise and front-loaded with the core action. There is zero waste or redundancy, making it efficient for quick understanding.

    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 mutation operation to cancel tasks), lack of annotations, no output schema, and minimal description, it's incomplete. It doesn't cover behavioral aspects, error handling, or output expectations, leaving gaps for safe and effective use by an AI agent.

    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 the parameter 'taskId' documented as '任务ID' (task ID). The description doesn't add meaning beyond the schema, such as format examples or sourcing guidance. Baseline 3 is appropriate since the schema does the heavy lifting, but no extra value is added.

    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 '取消下载任务' (Cancel download task) clearly states the action (cancel) and target resource (download task). It distinguishes from siblings like 'download_video' (initiates download) and 'get_task_status' (checks status), though it doesn't explicitly differentiate them. The purpose is specific but lacks explicit sibling comparison.

    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 is provided. The description doesn't mention prerequisites (e.g., task must be active), exclusions (e.g., cannot cancel completed tasks), or relationships with siblings like 'get_task_status' for verification. Usage is implied only by the action name.

    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 full burden for behavioral disclosure. It states the basic action (download video) but doesn't describe what happens during execution: whether it's synchronous/asynchronous, if it creates background tasks, error handling, file system impacts, or performance characteristics. For a tool with 8 parameters and no annotation coverage, this leaves significant behavioral gaps.

    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 Chinese sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a download tool and front-loads the core functionality. Every word earns its place with zero waste.

    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?

    For a complex video download tool with 8 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (success/failure indicators, file paths, task IDs), doesn't mention behavioral aspects like async processing (suggested by sibling task tools), and provides minimal context beyond the basic action. The description should do more given the tool's complexity.

    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 8 parameters thoroughly with descriptions, defaults, enums, and required status. The description adds no parameter-specific information beyond what's in the schema. Baseline 3 is appropriate when schema does all the parameter documentation work.

    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 action ('下载' meaning download) and target resource ('指定URL的视频'), specifying it downloads video from a URL to a local path. It distinguishes from siblings like get_video_info (info retrieval) and list_tasks (task listing), but doesn't explicitly differentiate from cancel_task or get_task_status which are task management tools. The purpose is clear but sibling differentiation is implicit rather than explicit.

    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. While it's obvious this is for downloading videos (unlike get_video_info for info retrieval), there's no mention of prerequisites, constraints, or when to choose other tools like cancel_task for task management. Usage context is implied but not explicitly stated.

    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 gets video information without downloading, which implies a read-only operation, but doesn't disclose other traits such as rate limits, authentication needs, error handling, or what specific information is returned. This leaves significant gaps for an AI agent to understand the tool's behavior fully.

    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 extremely concise and front-loaded, consisting of a single, clear phrase: '获取视频信息(不下载)'. Every word earns its place by specifying the action, resource, and a key constraint, with zero waste or redundancy.

    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 moderate complexity (retrieving video info), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what information is returned (e.g., metadata, duration, format), potential errors, or how it interacts with sibling tools like 'list_tasks'. This leaves the AI agent with insufficient context for effective use.

    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 'url' clearly documented as '视频链接URL' (video link URL). The description adds no additional meaning beyond this, as it doesn't elaborate on URL formats, supported platforms, or validation rules. Given the high schema coverage, a baseline score of 3 is appropriate.

    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 ('获取' meaning 'get') and resource ('视频信息' meaning 'video information'), and explicitly distinguishes it from downloading ('不下载' meaning 'not download'). However, it doesn't differentiate from sibling tools like 'get_task_status' which might also retrieve information, so it's not a perfect 5.

    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 specifying 'not download', suggesting this tool is for metadata retrieval rather than file acquisition. However, it doesn't explicitly state when to use this versus alternatives like 'download_video' or 'list_tasks', nor does it provide context about prerequisites or exclusions, leaving some ambiguity.

    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 full burden. '列出所有下载任务' implies a read-only listing operation, but it doesn't disclose behavioral traits like whether it returns active/completed tasks, pagination, sorting, rate limits, authentication needs, or error conditions. For a listing tool with zero annotation coverage, this leaves significant gaps.

    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 phrase ('列出所有下载任务') that directly states the purpose with zero waste. It's appropriately sized for a simple listing tool and front-loaded with the core action.

    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 simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavior, output format, or usage context. For a listing tool, it should ideally clarify scope (e.g., all tasks vs. filtered) or return structure, but the absence of an output schema increases the burden slightly.

    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 tool has 0 parameters with 100% schema description coverage (empty schema). The description doesn't need to add parameter semantics, so it meets the baseline. No compensation is required, and it doesn't introduce confusion about parameters.

    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 '列出所有下载任务' (List all download tasks) clearly states the verb (list) and resource (download tasks). It distinguishes from siblings like 'cancel_task' (action) and 'get_task_status' (status query) by indicating it returns all tasks rather than specific ones. However, it doesn't explicitly differentiate from 'get_video_info' which might return different resource types.

    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_task_status' for specific task details or 'cancel_task' for task management. It doesn't mention prerequisites, context for usage, or any exclusions. The agent must infer usage from the tool name alone.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

video-fetch-mcp MCP server

Copy to your README.md:

Score Badge

video-fetch-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/pickstar-2002/video-fetch-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server