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BACH-AI-Tools

Ghibli Image Generator MCP Server

by BACH-AI-Tools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: 'example_response' appears to provide sample outputs or documentation, while 'ghibli_generate_image' performs the core image generation function. There is no overlap or ambiguity between these tools.

    Naming Consistency2/5

    The naming is inconsistent: 'example_response' uses snake_case but lacks a clear verb-action pattern, while 'ghibli_generate_image' uses snake_case with a more descriptive verb-noun structure. This mixed approach reduces predictability.

    Tool Count2/5

    With only 2 tools, this server feels under-scoped for an image generator domain. A typical image generation service would include tools for configuration, style control, or batch operations, making this count insufficient for robust functionality.

    Completeness1/5

    The toolset is severely incomplete for an image generator: it lacks essential operations like parameter adjustment, style selection, image editing, or retrieval of generated images. The 'example_response' tool does not contribute to core functionality, leaving major gaps.

  • Average 1.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 is passing

  • 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.

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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

  • Behavior1/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. The description 'example response' gives no insight into whether the tool is read-only, destructive, requires authentication, has rate limits, or any other behavioral traits. It fails to provide any useful context beyond the name.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    While the description is extremely concise with just two words, it is under-specified rather than efficiently informative. The brevity does not serve clarity or utility, as it fails to convey any meaningful content about the tool's function or use.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the lack of annotations, output schema, and a vague description, the description is completely inadequate for understanding the tool. It provides no context about complexity, behavior, or expected outcomes, making it insufficient for agent decision-making despite the simple parameterless schema.

    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, so the schema fully documents the lack of inputs. The description does not add any parameter information, which is acceptable given the baseline of 4 for zero parameters, as no compensation is needed for missing details.

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

    Purpose1/5

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

    The description 'example response' is a tautology that merely restates the tool name 'example_response' without providing any meaningful information about what the tool does. It lacks a specific verb, resource, or any indication of functionality, making it completely unhelpful for understanding the tool's purpose.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines1/5

    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, what context it applies to, or how it differs from the sibling tool 'ghibli_generate_image'. There is no mention of alternatives, prerequisites, or exclusions, leaving the agent with no usage information.

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

  • Behavior1/5

    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 but offers none. 'Ghibli generate' doesn't indicate whether this is a read or write operation, what permissions might be needed, whether it has side effects, rate limits, or what the output format might be. For a generation tool with zero annotation coverage, this is a critical gap.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    While extremely brief ('Ghibli generate'), this isn't true conciseness but rather under-specification. The two words don't form a complete thought and fail to convey necessary information. Every sentence should earn its place, but here the minimal content doesn't justify its existence as a helpful description.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity of an image generation tool with no annotations and no output schema, the description is completely inadequate. It doesn't explain what 'Ghibli' means, what kind of image is generated, what the output looks like, or any behavioral characteristics. The description fails to provide the minimal context needed for effective use.

    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 zero parameters (schema coverage 100%), so there's no need for parameter documentation in the description. The baseline for zero parameters is 4, as the description doesn't need to compensate for any parameter gaps. However, it doesn't reach 5 because it could optionally mention that no inputs are required.

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

    Purpose2/5

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

    The description 'Ghibli generate' is a tautology that essentially restates the tool name 'ghibli_generate_image' with minimal added meaning. It doesn't specify what resource is being generated (presumably an image based on the name) or what 'Ghibli' refers to (likely Studio Ghibli style). While it hints at generation, it lacks the specific verb+resource clarity needed for proper understanding.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines1/5

    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. There's no mention of context, prerequisites, or comparison with the sibling tool 'example_response'. Users must infer usage entirely from the tool name, which is insufficient for effective tool selection.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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