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Badribn0612

img-gen

by Badribn0612
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool 'generate_image' has a clear, singular purpose that cannot be confused with any other tool in the set.

    Naming Consistency5/5

    The single tool name 'generate_image' follows a clear verb_noun pattern, and since there are no other tools, consistency is inherently perfect with no deviations or mixed conventions to evaluate.

    Tool Count2/5

    A single tool is too few for a server named 'img-gen', which suggests a broader image generation domain. This minimal set feels thin and incomplete, as it lacks operations like editing, upscaling, or managing generated images, which are common in such domains.

    Completeness2/5

    The tool surface is severely incomplete for an image generation server. While 'generate_image' covers creation, there are obvious gaps such as no tools for editing images, retrieving past generations, or handling variations, which limits agent workflows and leads to dead ends.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues 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 full burden for behavioral disclosure. While 'Create images' implies a generative/mutation operation, the description lacks critical behavioral details: it doesn't mention authentication requirements, rate limits, cost implications, quality expectations, or what happens when generation fails. For a generative AI tool with zero annotation coverage, this is a significant gap.

    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 efficiently structured with clear sections (Args, Returns) and uses minimal sentences. The first sentence directly states the purpose, and subsequent sections provide necessary information without redundancy. However, the 'Args' section could be more integrated with the main description rather than appearing as a separate bullet.

    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 has an output schema (which covers return values) and only one parameter, the description is reasonably complete for basic understanding. However, for an AI image generation tool with no annotations, it should provide more context about behavioral characteristics like generation time, supported image formats, resolution options, or usage limitations to be truly complete.

    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 description provides basic semantic information about the single parameter ('A prompt to generate the image using image generation model'), but with 0% schema description coverage, this doesn't fully compensate. The description doesn't specify prompt format requirements, length constraints, style guidance, or what makes an effective prompt. Baseline 3 is appropriate as the description adds some value but doesn't fully address the coverage gap.

    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 'Create images based on the prompt' - a specific verb ('Create') and resource ('images') with the mechanism ('based on the prompt'). However, without sibling tools, there's no opportunity to differentiate from alternatives, so it cannot achieve a perfect score of 5.

    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 simply states what the tool does without any context about appropriate use cases, prerequisites, or limitations. There's no mention of when this tool would be preferred over other image generation methods.

    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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