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Server Quality Checklist

92%
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  • Latest release: v1.0.1

  • Disambiguation5/5

    Each tool has a distinct purpose: basic generation, background removal, upscaling, or both. Descriptions clearly differentiate them, leaving no ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent 'generate_' prefix with a descriptive suffix (_image, _clean, _hd, _pro), making the pattern predictable.

    Tool Count5/5

    With exactly 4 tools, the set covers the essential variations (basic, backgrounds removal, upscaling, and combined) without any excess or deficiency.

    Completeness5/5

    The tools cover the full advertised functionality: generating images, removing backgrounds, upscaling, and the combination. No obvious gaps exist for this scope.

  • Average 3.6/5 across 4 of 4 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
    • 4 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • 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.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

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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?

    Discloses cost ($0.35 USDC) and return format (transparent PNG URL), which adds value beyond the schema. However, without annotations, it omits behavioral constraints like rate limits, auth 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?

    Two sentences, front-loaded with action and result, then precise details. No wasted words.

    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?

    For a simple 2-param tool with no output schema, the description covers purpose, cost, and output format. However, missing usage context and edge-case guidance leaves it slightly incomplete.

    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 description adds minimal parameter-specific meaning beyond listing prompt and aspect. The background removal note adds context but is not tied to a specific parameter.

    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 generates an AI image with background removal and returns a transparent PNG. It uses specific verbs and resources, but does not contrast with sibling tools beyond the background removal hint.

    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 generate_hd, generate_image, or generate_pro. The description lacks context on preferred scenarios or 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?

    With no annotations provided, the description carries the full disclosure burden. It reveals the output format (PNG image URL), cost ($0.25 USDC), and automatic payment, which are valuable behavioral traits. However, it omits details like authorization requirements, rate limits, error behavior on invalid prompts, or confirmation of the default aspect ratio.

    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 succinct sentences with front-loaded purpose. No unnecessary words: first sentence states the action, second covers output and cost. Every sentence earns its place, and the structure is efficient for quick agent parsing.

    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 simplicity (3 parameters, no output schema), the description covers the key aspects: what it does, output format, and cost. It lacks an explicit default for aspect ratio (though likely '1:1') and does not clarify that affiliate_id is optional, but these are minor gaps. The cost disclosure is particularly valuable for agent decision-making.

    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%, with all three parameters described in the schema. The description adds no further param-specific details beyond what the schema provides. Baseline 3 is appropriate; the description does not enhance understanding of parameters but also does not miss critical information.

    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 explicitly states 'Generate a standard AI image from a text prompt', clearly identifying the verb (generate) and resource (standard AI image). The term 'standard' distinguishes it from sibling tools generate_clean, generate_hd, and generate_pro, which likely offer different quality levels.

    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 does not provide explicit guidance on when to use this tool versus its siblings (e.g., generate_hd for higher quality). The cost mention ($0.25) implies a pricing consideration, but no direct comparison or when-not-to-use advice is given. Usage context is only implied by the word 'standard'.

    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, the description discloses cost and blockchain, but lacks details on output format, authentication, side effects, or payment flow. It partially informs behavior but has 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, well-structured sentence that is front-loaded with the action. Every word adds value, with no extraneous content.

    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?

    Despite having a clear purpose, the description fails to explain the output type or payment mechanism. For a paid tool with no output schema, this is a significant omission.

    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 baseline is 3. The main description adds no extra parameter meaning beyond the schema definitions.

    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 'Generate' and resource 'top-tier AI image' with specific post-processing (background removal, 4x HD upscale). It differentiates from siblings by combining features that others likely lack.

    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 both background removal and upscale are needed, and mentions cost. However, it does not explicitly guide when to use this vs alternatives, nor does it state when not to use it.

    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, the description carries full burden. It discloses the cost ($0.50 USDC on Base mainnet) and that it returns a URL, but omits details like required authentication, idempotency, rate limits, or whether the URL is temporary. The cost is a notable behavioral trait.

    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 sentences with no filler. It front-loads the action and quality, then states return value and cost. Every word adds value.

    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 no output schema, the description mentions return of a URL but lacks details like expiration, file format, or resolution specifics. For a paid tool, more context on what the URL contains would be helpful. It is minimally adequate.

    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 baseline is 3. The description adds no additional meaning beyond the schema's parameter descriptions (prompt and aspect). The schema already explains prompt as text and aspect as enum with default.

    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 that the tool generates a premium AI image upscaled 4x HD and returns a high-resolution image URL. The verb, resource, and quality are specific, and 'HD' differentiates it from sibling tools like 'generate_clean' or 'generate_pro'.

    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 high-quality images with upscaling and mentions cost, but does not explicitly state when to use this tool versus alternatives like generate_clean or generate_image. No when-not-to-use guidance is provided.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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