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iconben

Z-Image Studio

by iconben
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The three tools have completely distinct purposes: generating images, listing generation history, and listing available models. There is no functional overlap, so an agent can clearly differentiate them.

    Naming Consistency5/5

    All tool names use lowercase snake_case with a clear verb-first pattern. Although 'generate' is a single verb while the others are 'list_*', the convention is consistent and predictable across the set.

    Tool Count4/5

    With only 3 tools, the server is on the minimal side but still covers the core functionality of an image generation service. It falls within the acceptable range for a focused tool, though a few more utilities could enhance its scope.

    Completeness3/5

    The server includes essential operations: generate, view history, and list models. However, it lacks common lifecycle operations such as deleting history entries, viewing a specific generation's details, or updating a generation, which may hinder some workflows.

  • Average 3.5/5 across 3 of 3 tools scored. Lowest: 2.7/5.

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

    • 1 of 3 community issues answered or closed in the last 6 months
    • 14 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under Apache 2.0.

  • 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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    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It fails to mention ordering, pagination behavior, or any side effects, leaving the agent with minimal insight into how the tool behaves beyond listing.

    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 a single efficient sentence that conveys purpose without extraneous words. However, it lacks structural elements like bullet points or explicit parameter explanations. It earns its place but is slightly under-developed.

    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 list tool with an output schema, the description is minimally adequate. It does not explain pagination, ordering, or the nature of the output, but the presence of the output schema reduces the burden. Missing usage context lowers completeness.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, and the description adds no information about the 'limit' and 'offset' parameters beyond what the schema already provides. The description must compensate for low coverage but does not.

    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 verb 'List' and the resource 'recent image generations history', effectively distinguishing it from sibling tools 'generate' and 'list_models'. However, 'recent' is ambiguous and not defined.

    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 is provided on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description simply states the function without context.

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

  • Behavior4/5

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

    With no annotations, the description fully discloses return structure (three content types), transport-dependent URI building, progress notifications for long-running operations, and client disconnection handling. It is transparent about key behaviors, though it omits side effects or auth requirements.

    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 front-loads the core purpose and uses structured bullet points for return type details and URI building priority. While somewhat verbose on transport specifics, it is well-organized and each section adds meaningful detail.

    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 6 parameters, no annotations, and existing output schema, the description covers the tool's operation and return structure but lacks constraints on parameter values (e.g., valid ranges for steps/dimensions) and usage examples. It is adequate but not complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema has 50% description coverage for 6 parameters. The description does not elaborate on any parameter meanings or constraints beyond the schema's own descriptions, failing to add value for the agent's understanding of parameters like seed, precision, etc.

    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 begins with a clear statement: 'Generate an image from a text prompt.' This provides a specific verb and resource, and the tool is easily distinguishable from its siblings (list_history, list_models).

    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 image generation but does not explicitly tell when to use this tool versus alternatives, nor does it provide conditions for not using it. No exclusions or alternative suggestions are given.

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

  • Behavior4/5

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

    With no annotations provided, the description fully carries the burden of disclosing behavior. It states the tool lists models and hardware recommendations, implying a read-only operation with no side effects. The behavior is clear for a simple list operation.

    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 sentence of nine words, front-loaded with the verb 'List'. Every word is necessary, and there is no extraneous 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?

    Given the tool has no parameters and an output schema exists (to describe return values), the description is largely sufficient. It covers the tool's purpose, though it lacks mention of when it should be called or any prerequisites.

    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?

    There are no parameters, and schema coverage is trivially 100%. The description adds no parameter information because none exist, meeting the baseline for high coverage.

    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 tool lists available image generation models and hardware recommendations. It uses a specific verb ('List') and resource ('image generation models'), and distinguishes from siblings ('generate' and 'list_history') by its function.

    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 does not explicitly state when to use this tool versus alternatives, but the context of sibling tools implies it is used before 'generate' to see available models. No 'when-not' or alternative references are 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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Glama performs regular codebase and documentation scans to:

  • 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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