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kira4094

Agnes Video MCP Server

by kira4094
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JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one creates a video generation task, the other polls for its result. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent 'agnesis_video_verb' pattern using snake_case. 'create' and 'poll' are clear and parallel verbs.

    Tool Count4/5

    With only 2 tools, the server is minimal but sufficient for the core asynchronous workflow of creating and polling a video task. A slight expansion (e.g., cancel or list) could be beneficial but is not necessary.

    Completeness4/5

    The tool surface covers the essential create-and-poll lifecycle for video generation. However, there is no way to cancel a task or list all tasks, which are minor gaps.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • 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

  • Behavior4/5

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

    With no annotations provided, the description carries full burden. It discloses the asynchronous nature, the constraint on num_frames (8n+1 pattern), and the two modes. It does not mention rate limits, permissions, or the exact return value (e.g., task ID), but the core behavioral traits are well conveyed.

    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 four sentences, front-loaded with purpose, and each sentence adds unique value: async nature, supported types, and frame constraint. No verbose or redundant phrasing.

    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 9 parameters, no output schema, and the complexity of an async creation tool, the description covers the essential workflow and key constraints. It does not describe the return value (e.g., task ID) or error handling, but the presence of agnes_video_poll mitigates the need for full return details.

    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%, so baseline is 3. The description adds context for num_frames (the 8n+1 pattern) and notes that mode is required if image_url is set, which is not in the schema. However, most parameter meaning is already clear from schema descriptions, so the description adds limited extra value.

    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 'Create a video generation task using Agnes Video v2.0', using a specific verb and resource. It distinguishes between text-to-video and image-to-video, and mentions the sibling tool agnes_video_poll for polling results, which clarifies its role in the workflow.

    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 explicitly states that video generation is asynchronous and advises to use agnes_video_poll to check results. It also specifies the conditions for text-to-video vs. image-to-video and the required num_frames pattern. However, it does not explicitly state when not to use the tool or mention alternatives.

    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?

    No annotations provided, so description carries full burden. It states returns status and video URL when completed, but does not disclose behavior for in-progress tasks, error handling, or rate limits. Basic transparency but 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?

    Three sentences, front-loaded with verb and resource. No wasted words, each sentence adds essential information.

    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?

    Covers purpose, input origin, and return value. For a simple polling tool with one parameter and no output schema, it is fairly complete. Lacks details on status values or polling behavior, but adequate.

    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?

    Schema coverage is 100%, so baseline is 3. Description adds value by explaining that task_id is returned by agnes_video_create, providing context beyond the schema's description.

    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?

    Description clearly states the verb 'poll' and the resource 'video generation task by task_id'. It distinguishes from the sibling 'agnes_video_create' by specifying that this tool checks progress, not creates.

    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?

    Description explains when to use (after creation) and where to get the input (task_id from agnes_video_create). It implies not to use before creation but lacks explicit alternatives 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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