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peypey84

Agnes Media MCP Server

by peypey84
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool targets a distinct operation: image generation, video generation (async), and video status retrieval. No overlap in purpose.

    Naming Consistency5/5

    All tools follow a consistent 'agnes_verb_noun' pattern with underscores (e.g., generate_image, get_video_status). No mixing of styles.

    Tool Count4/5

    3 tools is minimal but appropriate for the focused scope of image and video generation. Could be expanded with list/delete utilities, but not underpowered.

    Completeness3/5

    Covers core generation and status polling, but lacks list/delete or cancel operations for videos, and no tool for managing images beyond generation.

  • Average 4.2/5 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 status not available

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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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?

    No annotations are provided, so the description carries full burden. It discloses that images are saved locally and a preview is returned inline (only for small files). However, it omits details such as whether the tool is blocking, if it overwrites existing files, any cost or rate limits, and the exact return format beyond a preview.

    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 with no extraneous information. The description is front-loaded with the main purpose and is structured logically: mode explanation, then outcome.

    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?

    Covers the core functionality and modes. However, it lacks details on output file naming, handling of duplicate file names, limitations on prompt length or image_urls count, and the structure of the inline preview response. Given no output schema, more completeness on return values would be 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 coverage is 100%, so baseline is 3. The description adds value by explaining the mode switch tied to image_urls and the size limitation for inline_preview. Other parameters (size, ratio, prompt) are not elaborated beyond the schema, so no significant added meaning.

    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 it generates images using a specific model (Agnes Image 2.1 Flash). It distinguishes between text-to-image and image-to-image modes, and given sibling tools are for video, the tool's purpose is unambiguous and distinct.

    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 explains when to use each mode: text-to-image by default, image-to-image when image_urls are provided. It does not explicitly state when not to use the tool or provide alternatives, but the context is clear enough.

    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?

    Discloses optional download, but lacks details on side effects (e.g., filesystem changes) or error states. No annotations, so description should provide more behavioral context.

    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?

    Two efficient sentences, front-loaded with purpose, no wasted words.

    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?

    Explains return values (status, URL) and optional download, but lacks output format details. Adequate for a simple poll 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 covers 100% of parameters with descriptions. Description adds little beyond schema; baseline 3 is appropriate.

    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 tool checks status of a video task, returns progress and URL, and optionally downloads. Distinguishes from sibling generation tools.

    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?

    Clear context: used after task creation to check status. No explicit exclusions or alternatives, but implied by sibling tools.

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

  • Behavior5/5

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

    With no annotations, the description fully discloses async nature, blocking duration (wait_seconds), return behavior (local path vs. video_id), and constraints on num_frames (<=441, 8n+1). No contradictions.

    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?

    Single paragraph is functional and every sentence adds value, but could be more structured (e.g., bullet points for modes). Not too long, well-focused.

    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 complex tool with 10 params and no output schema, description covers async flow, constraints, mode selection, and return values. Lacks explicit error handling details but sufficient for 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?

    Schema coverage is 100% (baseline 3), but description adds significant value: explains mode parameter usage in prose, clarifies wait_seconds logic, and provides frame count examples (81≈3s, etc.) beyond schema descriptions.

    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 it starts async video generation (Agnes V2.0), enumerates three modes (text-to-video, image-to-video, keyframe transitions), and distinguishes from siblings (agnes_get_video_status for polling, agnes_generate_image for images).

    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 explains when to use each mode, describes wait_seconds inline behavior, and directs polling with agnes_get_video_status upon timeout. Provides clear when-to-use and when-not-to-use 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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  • Evaluate tool definition quality.

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